paper_id string | claim_id string | claim string | label string | caption string | evi_type string | evi_path string | context string | domain string | use_context string | operation string | paper_path string | detail_others string | license_name string | license_url string | claim_id_pair string | evi_path_original string |
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2025.naacl-long.15 | val_fig_0158 | By repeating the wrapping instructions of ChatGPT thrice, we observed a decrease in the BiasF_{o} (ii) score presented in Fig. 6 . Combining (i) and (ii) suggests that this strategy is an effective mitigation. | Supported | Figure 6: More demonstrations and repeating format instructions mitigate format bias. Finetuning mostly eliminates the format bias. The performance is reported using ChatGPT on MMLU (Appx.- Tab. 18 for num. results). | figure | figures/dev/val_fig_0158.png | We found that repeating instructions generally increases FI scores (i) across most formats except “Placeholder”, which can consequently lessen the mode’s token bias towards format instructions ( Section 5.1 ). Using our two proposed criteria for effective format bias mitigation in Section 3.2 , it is worth examining if... | nlp | no | Legend Swap | papers/dev/nlp_2025.naacl-long.15.json | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 0243 | null | |
2025.naacl-long.15 | val_fig_0159 | By repeating the wrapping instructions of ChatGPT thrice, we observed a decrease in the BiasF_{o} (ii) score presented in Fig. 6 . Combining (i) and (ii) suggests that this strategy is an effective mitigation. | Refuted | Figure 6: More demonstrations and repeating format instructions mitigate format bias. Finetuning mostly eliminates the format bias. The performance is reported using ChatGPT on MMLU (Appx.- Tab. 18 for num. results). | figure | figures/dev/val_fig_0159.png | We found that repeating instructions generally increases FI scores (i) across most formats except “Placeholder”, which can consequently lessen the mode’s token bias towards format instructions ( Section 5.1 ). Using our two proposed criteria for effective format bias mitigation in Section 3.2 , it is worth examining if... | nlp | no | Legend Swap | papers/dev/nlp_2025.naacl-long.15.json | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 0243 | null | |
2025.naacl-short.5 | val_fig_0160 | In numerical domains (Number, Number+Text), LLMs are much more accurate in QA versus RQA , revealing a clear abduction weakness. | Supported | Figure 2: LLM RQA ( blue ) and QA ( red ) accuracy with 95% CIs for metric error rate. LLMs are much weaker in abductive RQA in numerical settings (Number/Number+Text), but in text settings (Easy/Hard Entity), deductive QA is slightly weaker. | figure | figures/dev/val_fig_0160.png | We first see if RQA ( red , no stripe) or QA ( blue , striped) is consistently harder for LLMs (Figure 2 ). | nlp | no | Legend Swap | papers/dev/nlp_2025.naacl-short.5.json | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 0244 | null | |
2025.naacl-short.5 | val_fig_0161 | In numerical domains (Number, Number+Text), LLMs are much more accurate in QA versus RQA , revealing a clear abduction weakness. | Refuted | Figure 2: LLM RQA ( blue ) and QA ( red ) accuracy with 95% CIs for metric error rate. LLMs are much weaker in abductive RQA in numerical settings (Number/Number+Text), but in text settings (Easy/Hard Entity), deductive QA is slightly weaker. | figure | figures/dev/val_fig_0161.png | We first see if RQA ( red , no stripe) or QA ( blue , striped) is consistently harder for LLMs (Figure 2 ). | nlp | no | Legend Swap | papers/dev/nlp_2025.naacl-short.5.json | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 0244 | null | |
2025.naacl-short.5 | val_fig_0163 | Number+Text a have lower Dolma token counts when RQA fails (Fig 4 ), so LLMs struggle to recall long-tail numerical facts Kandpal et al. ( 2023 ) . | Supported | Figure 4: Answer token count and question difficulty of when RQA succeeds/fails, averaged over all LLMs. | figure | figures/dev/val_fig_0163.png | nlp | no | Legend Swap | papers/dev/nlp_2025.naacl-short.5.json | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 0245 | null | ||
2025.naacl-long.17 | val_fig_0165 | Our findings show that: (1) As expected, using the most closely related language as the auxiliary example bank yields the best performance, as it provides relevant guidance to the LLM, and (2) Selecting a random or unrelated language results in little to no improvement, with performance remaining close to the relevance... | Supported | Figure 1: Token-F1 evaluation on the cross-lingual QA task in Manipuri, with retrievers trained using different auxiliary high-resource example banks: (1) Closely related language, (2) Random language, and (3) Unrelated language. | figure | figures/dev/val_fig_0165.png | We evaluate three setups: (1) Our method (Alg. 2 ) of selecting the most closely related language as the auxiliary example bank, (2) selecting a random language, and (3) selecting the most unrelated language. We show the few-shot performance on cross-lingual QA task in Figure 1 , with retrievers trained under each setu... | nlp | no | Legend Swap | papers/dev/nlp_2025.naacl-long.17.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0246 | null | |
2023.emnlp-main.8 | val_fig_0167 | ChatGPT is affected by the primacy effect : ChatGPT tends to select labels in earlier positions in the prompt (see Fig. 1 ), which present clear bias with respect to the label order. | Supported | Figure 1: Primacy Effect of ChatGPT : ChatGPT tends to return labels in earlier positions as the answer. This plot shows the distribution of ChatGPT’s predicted label indices in TACRED (42 classes), where we randomly shuffle labels before every prediction (see Sec. 2.2 ). | figure | figures/dev/val_fig_0167.png | nlp | other sources | Supported_claim_only | papers/dev/nlp_2023.emnlp-main.8.json | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | no pair | null | ||
2023.emnlp-main.8 | val_fig_0168 | We visualize the distributions in Fig. 4 .
Notably, the distribution of ChatGPT’s predictions consistently deviates from the uniform distribution, displaying a consistent bias towards smaller indices across different datasets. | Supported | (a) TACRED; (b) TACREV; (c) Re-TACRED; (d) Banking77; The distribution of predicted indices of the test instances with label shuffling before every prediction. | figure | figures/dev/val_fig_0168.png | The empirical results in Section 3.2 indicate that ChatGPT’s predictions are affected by label order. To deeper delve into the effects of label orders on ChatGPT, we analyze the distribution of predicted label indices (e.g., if the prediction is the first label, the label index is 1 ), as introduced in Section 2.2 . | nlp | no | Supported_claim_only | papers/dev/nlp_2023.emnlp-main.8.json | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | no pair | null | |
2023.emnlp-main.19 | val_fig_0169 | Loss method outperformed all others in approximately one-third of the 35 settings examined, as indicated in Figure 5 (left). | Supported | Figure 5: Summary of subset selection strategies performances from. Left: percentage of times each strategy gets the best performance out of 35 settings (across each of the 7 languages and 5 \hat{D}^{Syn}_{train} sizes). Right: bootstrapped confidence intervals for the percentages on the left. | figure | figures/dev/val_fig_0169.png | Effective subsets have high diversity and predictive uncertainty . Our analysis reveals statistically significant differences between the subset selection strategies, highlighting the effectiveness of the hybrid approaches ( UMT/EMT+ Loss ) that consider both diversity and predictive uncertainty. Among the strategies t... | nlp | no | Category Swap | papers/dev/nlp_2023.emnlp-main.19.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0247 | null | |
2023.emnlp-main.19 | val_fig_0171 | Furthermore, selecting datapoints based solely on high predictive uncertainty without considering diversity ( HighLoss ) is an ineffective strategy, having the second lowest proportion of wins (Fig. 5 , right). | Supported | Figure 5: Summary of subset selection strategies performances from. Left: percentage of times each strategy gets the best performance out of 35 settings (across each of the 7 languages and 5 \hat{D}^{Syn}_{train} sizes). Right: bootstrapped confidence intervals for the percentages on the left. | figure | figures/dev/val_fig_0171.png | nlp | no | Category Swap | papers/dev/nlp_2023.emnlp-main.19.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0248 | null | ||
2023.emnlp-main.1 | val_fig_0173 | As shown in Figure 3 , \mathcal{Q}_{\text{All}} hardly outperforms the RAG baseline ( 64.3\rightarrow 64.5 ). | Supported | Figure 3: Comparison of different warmup strategies. Scores are reported on the StrategyQA dev set. | figure | figures/dev/val_fig_0173.png | nlp | no | Category Swap | papers/dev/nlp_2023.emnlp-main.1.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0249 | null | ||
2023.emnlp-main.1 | val_fig_0174 | As shown in Figure 3 , \mathcal{Q}_{\text{All}} hardly outperforms the RAG baseline ( 64.3\rightarrow 64.5 ). | Refuted | Figure 3: Comparison of different warmup strategies. Scores are reported on the StrategyQA dev set. | figure | figures/dev/val_fig_0174.png | nlp | no | Category Swap | papers/dev/nlp_2023.emnlp-main.1.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0249 | null | ||
2023.emnlp-main.1 | val_fig_0175 | As shown in Figure 4 , IAG- Student achieves the best performance with the statement number between 5 and 7. | Supported | Figure 4: Scores of IAG- Student on StrategyQA dev set with different numbers of knowledge statements. | figure | figures/dev/val_fig_0175.png | Our implementation of IAG samples 5 knowledge statements to feed into the generator. To justify this design choice, we evaluate the performance of IAG- Student with varying statement numbers. | nlp | no | Graph Flip | papers/dev/nlp_2023.emnlp-main.1.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0250 | null | |
2024.eacl-short.7 | val_fig_0178 | There are more extrinsic errors introduced in the news domain compared to the niche domain datasets. | Supported | (a) Prevalence of factual errors in each of domains; (b) Distribution of error categories across domains; Distribution of errors and error categories across domains | figure | figures/dev/val_fig_0178.png | We next characterize the distribution of error categories in factually inconsistent summaries generated by models across the domains considererd. Figure 1(b) reports the distribution of error categories for both models. 3 3 3 Model-specific distributions are in Appendix A.6 | nlp | no | Legend Swap | papers/dev/nlp_2024.eacl-short.7.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0251 | null | |
2024.eacl-short.7 | val_fig_0179 | There are more extrinsic errors introduced in the news domain compared to the niche domain datasets. | Refuted | (a) Prevalence of factual errors in each of domains; (b) Distribution of error categories across domains; Distribution of errors and error categories across domains | figure | figures/dev/val_fig_0179.png | We next characterize the distribution of error categories in factually inconsistent summaries generated by models across the domains considererd. Figure 1(b) reports the distribution of error categories for both models. 3 3 3 Model-specific distributions are in Appendix A.6 | nlp | no | Legend Swap | papers/dev/nlp_2024.eacl-short.7.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0251 | null | |
2024.eacl-long.14 | val_fig_0180 | Even for languages unseen in the pre-training corpus of XLM-R, it outperforms Glot-500 in most cases as long as the written scripts are seen. | Supported | Figure 2: Fully supervised Model Performance. We group languages by whether they and their scripts are seen in the pre-training corpus of XLM-R. Languages are ordered by the XLM-R performance in every group. | figure | figures/dev/val_fig_0180.png | Figure 2 compares MLP, XLM-R and Glot-500 models based on language and script coverage in pre-training based on four groups: (1) language seen, script seen in XLM-R (2) language unseen, script seen in XLM-R (3) script unseen in XLM-R, language seen in Glot-500 (4) script unseen by both models. The results in each group... | nlp | no | Legend Swap | papers/dev/nlp_2024.eacl-long.14.json | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 0252 | null | |
2024.eacl-long.14 | val_fig_0181 | Even for languages unseen in the pre-training corpus of XLM-R, it outperforms Glot-500 in most cases as long as the written scripts are seen. | Refuted | Figure 2: Fully supervised Model Performance. We group languages by whether they and their scripts are seen in the pre-training corpus of XLM-R. Languages are ordered by the XLM-R performance in every group. | figure | figures/dev/val_fig_0181.png | Figure 2 compares MLP, XLM-R and Glot-500 models based on language and script coverage in pre-training based on four groups: (1) language seen, script seen in XLM-R (2) language unseen, script seen in XLM-R (3) script unseen in XLM-R, language seen in Glot-500 (4) script unseen by both models. The results in each group... | nlp | no | Legend Swap | papers/dev/nlp_2024.eacl-long.14.json | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 0252 | null | |
2024.eacl-long.14 | val_fig_0182 | We observe that all languages that are included in the pre-training corpus of XLM-R, the cross-lingual transfer performs similarly to fully supervised methods. | Supported | Figure 5: Comparison of Various Scenarios. We group languages by whether they and their scripts are seen in the pre-training corpus of XLM-R. Languages are ordered by the XLM-R fully-supervised performance in every group. | figure | figures/dev/val_fig_0182.png | Here, we compare the performance between cross-lingual transfer and fully-supervised methods. | nlp | no | Legend Swap | papers/dev/nlp_2024.eacl-long.14.json | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 0253 | null | |
2024.eacl-long.14 | val_fig_0183 | The advantage of fully supervised methods over cross-lingual transfer becomes prominent mainly when the target language is not included in the pre-training corpus of XLM-R and its script is included. | Supported | Figure 5: Comparison of Various Scenarios. We group languages by whether they and their scripts are seen in the pre-training corpus of XLM-R. Languages are ordered by the XLM-R fully-supervised performance in every group. | figure | figures/dev/val_fig_0183.png | Here, we compare the performance between cross-lingual transfer and fully-supervised methods. We observe that all languages that are included in the pre-training corpus of XLM-R, the cross-lingual transfer performs similarly to fully supervised methods. The best source language for cross-lingual transfer is, surprising... | nlp | no | Supported_claim_only | papers/dev/nlp_2024.eacl-long.14.json | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | no pair | null | |
2024.acl-short.16 | val_fig_0184 | As we can see in Fig. 2 , the model with r=4 , yields poorer performance, highlighting the need for high rank for the frozen tensors. | Supported | Figure 2: Performance of ELoRA with two different ranks of the frozen projection matrices. | figure | figures/dev/val_fig_0184.png | To understand the high rank requirement for the frozen projection metrices in ELoRA, we conduct two sets of fine-tuning on SST-2 and MRPC, with ELoRA having rank ( r ) of 1024 and 4, respectively. | nlp | no | Legend Swap | papers/dev/nlp_2024.acl-short.16.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0254 | null | |
2024.bionlp-1.4 | val_fig_0186 | However, in the second experiment, when supplemented with Chain of Thought (CoT) prompting Wei et al. ( 2022 ) , the performance of these smaller models markedly improved, demonstrating how the step-by-step reasoning process aids in decomposing the complex task of propagation into manageable segments. | Supported | Figure 3: Effectiveness of ICL vs. CoT Strategies Across Multiple LLMs. | figure | figures/dev/val_fig_0186.png | We performed two sets of experiments. The first involved providing In-Context Learning (ICL) examples Min et al. ( 2022 ) and measuring accuracy, as illustrated in Figure 3 . Larger models such as GPT-4 and Opus yielded strong results, whereas smaller models like Mixtral-8X7B and GPT-3.5 exhibited suboptimal performanc... | nlp | yes | Supported_claim_only | papers/dev/nlp_2024.bionlp-1.4.json | CC BY-NC-SA 4.0 | http://creativecommons.org/licenses/by-nc-sa/4.0/ | no pair | null | |
2024.bionlp-1.4 | val_fig_0187 | Opus and GPT-4 emerged as the top performers, achieving approximately 94% accuracy when CoT prompting was combined with ICL examples, as illustrated in Figure 3 . | Supported | Figure 3: Effectiveness of ICL vs. CoT Strategies Across Multiple LLMs. | figure | figures/dev/val_fig_0187.png | We conducted an evaluation of the Propagator Agent utilizing various LLMs, with a particular emphasis on metrics such as accuracy and latency. | nlp | no | Legend Swap | papers/dev/nlp_2024.bionlp-1.4.json | CC BY-NC-SA 4.0 | http://creativecommons.org/licenses/by-nc-sa/4.0/ | 0255 | null | |
2024.bionlp-1.4 | val_fig_0188 | Opus and GPT-4 emerged as the top performers, achieving approximately 94% accuracy when CoT prompting was combined with ICL examples, as illustrated in Figure 3 . | Refuted | Figure 3: Effectiveness of ICL vs. CoT Strategies Across Multiple LLMs. | figure | figures/dev/val_fig_0188.png | We conducted an evaluation of the Propagator Agent utilizing various LLMs, with a particular emphasis on metrics such as accuracy and latency. | nlp | no | Legend Swap | papers/dev/nlp_2024.bionlp-1.4.json | CC BY-NC-SA 4.0 | http://creativecommons.org/licenses/by-nc-sa/4.0/ | 0255 | null | |
2024.acl-short.9 | val_fig_0191 | As expected, p(\boldsymbol{x|y}) shows a higher correlation with accuracy than fluency, and p(\boldsymbol{y}) shows the opposite pattern. | Supported | Figure 5: \text{accuracy}_{M} and \text{fluency}_{M} predict human accuracy and fluency ratings for RLTC and WMT submissions to the general translation task in 2022 and 2023. zhen and ende refer to Chinese-English and English-German language pairs. All correlations reported are significant ( p<.001 ). | figure | figures/dev/val_fig_0191.png | A similar conclusion is suggested by Figure 5 , which shows Pearson correlations of translation probability ( p(\boldsymbol{y|x}) ), \text{accuracy}_{M} ( p(\boldsymbol{x|y}) ) and \text{fluency}_{M} ( p(\boldsymbol{y}) ) with human ratings of accuracy and fluency for RLTC and MTMQM. 9 9 9 Values are ranked by percenti... | nlp | no | Legend Swap | papers/dev/nlp_2024.acl-short.9.json | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 0256 | null | |
2025.naacl-long.14 | val_fig_0197 | Both results are shown in Figure 6 . we observe that Kendall-Tau ( \tau ) correlations increase linearly with queries and pairwise judgments. | Supported | Figure 6: Sampling experiments to reduce computation cost. (left) reduces the number of queries whereas (right) reduces the pairwise judgments. | figure | figures/dev/val_fig_0197.png | Non-exhaustive pairwise comparisons lead to performance degradation. Exhaustive pairwise comparisons across a subset of 19 models in \mirage using GPT-4o as a teacher for “all” queries is quite expensive. To avoid this, we investigate whether really (all) pairwise exhaustive comparisons are necessary. We utilize two sa... | nlp | no | Legend Swap | papers/dev/nlp_2025.naacl-long.14.json | CC BY-SA 4.0 | http://creativecommons.org/licenses/by-sa/4.0/ | 0257 | null | |
2025.naacl-long.14 | val_fig_0198 | Both results are shown in Figure 6 . we observe that Kendall-Tau ( \tau ) correlations increase linearly with queries and pairwise judgments. | Refuted | Figure 6: Sampling experiments to reduce computation cost. (left) reduces the number of queries whereas (right) reduces the pairwise judgments. | figure | figures/dev/val_fig_0198.png | Non-exhaustive pairwise comparisons lead to performance degradation. Exhaustive pairwise comparisons across a subset of 19 models in \mirage using GPT-4o as a teacher for “all” queries is quite expensive. To avoid this, we investigate whether really (all) pairwise exhaustive comparisons are necessary. We utilize two sa... | nlp | no | Legend Swap | papers/dev/nlp_2025.naacl-long.14.json | CC BY-SA 4.0 | http://creativecommons.org/licenses/by-sa/4.0/ | 0257 | null | |
2025.naacl-long.14 | val_fig_0199 | In summary, an exhaustive pairwise comparison and a sufficient amount of queries, such as 100, is necessary without impacting the leaderboard rankings. | Supported | Figure 6: Sampling experiments to reduce computation cost. (left) reduces the number of queries whereas (right) reduces the pairwise judgments. | figure | figures/dev/val_fig_0199.png | Non-exhaustive pairwise comparisons lead to performance degradation. Exhaustive pairwise comparisons across a subset of 19 models in \mirage using GPT-4o as a teacher for “all” queries is quite expensive. To avoid this, we investigate whether really (all) pairwise exhaustive comparisons are necessary. We utilize two sa... | nlp | other sources | Supported_claim_only | papers/dev/nlp_2025.naacl-long.14.json | CC BY-SA 4.0 | http://creativecommons.org/licenses/by-sa/4.0/ | no pair | null | |
2025.naacl-long.15 | val_fig_0200 | 10 . From Fig. 4 -left, we notice that Mistral exhibits the most bias, with the BiasF_{o} value ( Eq. 8 ) of 353.80\%^{2} . | Supported | (a); (b); Average EstTrueF1 (SemEval2017) and EstTrueMAP (SciDocsRR) ( Section 3.1 ) across models (left) and benchmarks (right) showing performance difference of LLMs across 4 widely used list formats. | figure | figures/dev/val_fig_0200.png | Fig. 4 displays the key findings of our evaluation across models and datasets with numerical results in Appx.- Tab. | nlp | other sources | Legend Swap | papers/dev/nlp_2025.naacl-long.15.json | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 0258 | null | |
2025.naacl-long.15 | val_fig_0201 | 10 . From Fig. 4 -left, we notice that Mistral exhibits the most bias, with the BiasF_{o} value ( Eq. 8 ) of 353.80\%^{2} . | Refuted | (a); (b); Average EstTrueF1 (SemEval2017) and EstTrueMAP (SciDocsRR) ( Section 3.1 ) across models (left) and benchmarks (right) showing performance difference of LLMs across 4 widely used list formats. | figure | figures/dev/val_fig_0201.png | Fig. 4 displays the key findings of our evaluation across models and datasets with numerical results in Appx.- Tab. | nlp | other sources | Legend Swap | papers/dev/nlp_2025.naacl-long.15.json | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 0258 | null | |
2025.naacl-long.15 | val_fig_0202 | In contrast, ChatGPT and Gemma show much lower bias, with values of 7.08\%^{2} and 1.32\%^{2} , respectively. | Supported | (a); (b); Average EstTrueF1 (SemEval2017) and EstTrueMAP (SciDocsRR) ( Section 3.1 ) across models (left) and benchmarks (right) showing performance difference of LLMs across 4 widely used list formats. | figure | figures/dev/val_fig_0202.png | Fig. 4 displays the key findings of our evaluation across models and datasets with numerical results in Appx.- Tab. 10 . From Fig. 4 -left, we notice that Mistral exhibits the most bias, with the BiasF_{o} value ( Eq. 8 ) of 353.80\%^{2} . | nlp | other sources | Legend Swap | papers/dev/nlp_2025.naacl-long.15.json | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 0259 | null | |
2024.acl-short.16 | val_fig_0205 | Compared to ELoRA we can yield up to 1.86\times and 2.96\times runtime and FLOPs improvement while remain comparable with LoRA in these two metrices. | Supported | Figure 3: A comparison of various system performance between LoRA, ELoRA, and AFLoRA. | figure | figures/dev/val_fig_0205.png | Runtime & FLOPs Comparison. Fig. 3 shows the comparison of the normalized average training runtime, normalized FLOPs, and normalized trainable parameters. For AFLoRA, we average the training time, FLOPs, and trainable parameters over six GLUE datasets (except the MNLI and QQP datasets). Note, for LoRA and ELoRA, the tr... | nlp | other sources | Legend Swap | papers/dev/nlp_2024.acl-short.16.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0260 | null | |
2024.acl-short.16 | val_fig_0206 | Compared to LoRA we yield 9.5\times parameter reduction, while remain comparable with ELoRA. | Supported | Figure 3: A comparison of various system performance between LoRA, ELoRA, and AFLoRA. | figure | figures/dev/val_fig_0206.png | Runtime & FLOPs Comparison. Fig. 3 shows the comparison of the normalized average training runtime, normalized FLOPs, and normalized trainable parameters. For AFLoRA, we average the training time, FLOPs, and trainable parameters over six GLUE datasets (except the MNLI and QQP datasets). Note, for LoRA and ELoRA, the tr... | nlp | other sources | Legend Swap | papers/dev/nlp_2024.acl-short.16.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0261 | null | |
2024.acl-short.16 | val_fig_0207 | In specific, we illustrate the specific number of iterations required before freezing each component in Fig. 5 . Interestingly, as can be seen from the figure, analysis reveals that the down-projection matrix parallel the intermediate linear layer require longer training duration prior to being frozen, as compared to t... | Supported | Figure 5: Visualization of freezing iterations for each layer. ‘out’ and ‘inter’ refer to the second and the first MLP layer of the FFN, respectively. ‘A’ and ‘B’ represent the down-projection and up-projection matrix, respectively. The darker the color, the more iterations the matrix has to go through before freezing. | figure | figures/dev/val_fig_0207.png | Discussion on Freezing Trend. We use the RTE dataset as a case study, to understand the freezing trend of the PMs across different layers. | nlp | other sources | Graph Flip | papers/dev/nlp_2024.acl-short.16.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0262 | null | |
16727 | val_tab_0417 | These variables include age ( p = 0.002), having children ( p = 0.002), being related to the health sector ( p = 0.001), perception of the efficacy and protective effect of the booster dose (<0.001), as well as fear of adverse effects ( p < 0.001) were significantly associated with the IBV ( Table 2 ). | Supported | Table 2: Bivariate analysis of study characteristics according to intention to be vaccinated with the booster dose COVID-19 in Peru, July 2022 (n= 924). | table | tables_png/dev/val_tab_0417.png | Several variables were found to be significantly associated with the intention to be vaccinated (IBV), as detailed in Table 2 . | peerj | yes | Change the cell values | papers/dev/peerj_16727.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0263 | tables/dev/val_tab_0417.html | |
18098 | val_tab_0418 | These findings demonstrate that BiAMIL is a more accurate and reliable method for estimating the BRCA status compared to the traditional ResNet 34 model. | Supported | Table 2: The performance of the BiAMIL in predictingBRCAPV in hormone receptor + and triple-negative breast cancer. | table | tables_png/dev/val_tab_0418.png | The BiAMIL model was developed by first inputting normalized tiles into the ResNet 34 model to extract 1,000-dimensional features. These features were then aggregated using a Bi-directional self-attention mechanism approach. Subsequently, the aggregated feature matrix was fed into a multilayer perceptron (MLP) classifi... | peerj | yes | Change the cell values | papers/dev/peerj_18098.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0264 | tables/dev/val_tab_0418.html | |
18098 | val_tab_0419 | These findings demonstrate that BiAMIL is a more accurate and reliable method for estimating the BRCA status compared to the traditional ResNet 34 model. | Refuted | Table 2: The performance of the BiAMIL in predictingBRCAPV in hormone receptor + and triple-negative breast cancer. | table | tables_png/dev/val_tab_0419.png | The BiAMIL model was developed by first inputting normalized tiles into the ResNet 34 model to extract 1,000-dimensional features. These features were then aggregated using a Bi-directional self-attention mechanism approach. Subsequently, the aggregated feature matrix was fed into a multilayer perceptron (MLP) classifi... | peerj | yes | Change the cell values | papers/dev/peerj_18098.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0264 | tables/dev/val_tab_0419.html | |
17459 | val_tab_0420 | As shown in Table 2 , there was a statistically significant difference in the frequency of colds experienced by middle-aged and elderly individuals with varying levels of weekly PA ( P < 0.001). | Supported | Table 2: Cold in middle-aged and elderly with different characteristics. | table | tables_png/dev/val_tab_0420.png | peerj | no | Change the cell values | papers/dev/peerj_17459.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0265 | tables/dev/val_tab_0420.html | ||
17459 | val_tab_0421 | As shown in Table 2 , there was a statistically significant difference in the frequency of colds experienced by middle-aged and elderly individuals with varying levels of weekly PA ( P < 0.001). | Refuted | Table 2: Cold in middle-aged and elderly with different characteristics. | table | tables_png/dev/val_tab_0421.png | peerj | no | Change the cell values | papers/dev/peerj_17459.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0265 | tables/dev/val_tab_0421.html | ||
17459 | val_tab_0422 | However, no significant differences were found in cold frequency between participants of different sexes or age groups ( P > 0.05). | Supported | Table 2: Cold in middle-aged and elderly with different characteristics. | table | tables_png/dev/val_tab_0422.png | As shown in Table 2 , there was a statistically significant difference in the frequency of colds experienced by middle-aged and elderly individuals with varying levels of weekly PA ( P < 0.001). | peerj | no | Change the cell values | papers/dev/peerj_17459.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0266 | tables/dev/val_tab_0422.html | |
17459 | val_tab_0423 | However, no significant differences were found in cold frequency between participants of different sexes or age groups ( P > 0.05). | Refuted | Table 2: Cold in middle-aged and elderly with different characteristics. | table | tables_png/dev/val_tab_0423.png | As shown in Table 2 , there was a statistically significant difference in the frequency of colds experienced by middle-aged and elderly individuals with varying levels of weekly PA ( P < 0.001). | peerj | no | Change the cell values | papers/dev/peerj_17459.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0266 | tables/dev/val_tab_0423.html | |
18472 | val_tab_0424 | Fifty healthy students from University Europea de Madrid volunteered for the study Table 1 . | Supported | Table 1: Descriptive data. | table | tables_png/dev/val_tab_0424.png | peerj | no | Change the cell values | papers/dev/peerj_18472.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0267 | tables/dev/val_tab_0424.html | ||
17155 | val_tab_0426 | No study demonstrated sufficient validity for TMS biomarker use ( Bielekova & Martin, 2004 ). | Supported | Table 6: Biomarker assessment. | table | tables_png/dev/val_tab_0426.png | See Table 6 for a condensed summary of biomarker assessment findings and Table S6 for more detailed results. | peerj | other sources | Change the cell values | papers/dev/peerj_17155.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0268 | tables/dev/val_tab_0426.html | |
17155 | val_tab_0427 | No study demonstrated sufficient validity for TMS biomarker use ( Bielekova & Martin, 2004 ). | Refuted | Table 6: Biomarker assessment. | table | tables_png/dev/val_tab_0427.png | See Table 6 for a condensed summary of biomarker assessment findings and Table S6 for more detailed results. | peerj | other sources | Change the cell values | papers/dev/peerj_17155.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0268 | tables/dev/val_tab_0427.html | |
19471 | val_tab_0429 | In addition, significant differences were observed in all SU and SD kinematics except for KHD during SD. | Supported | Table 1: Participants characteristics. | table | tables_png/dev/val_tab_0429.png | A total of 43 recreational table tennis players were included in this study, with 44 and 42 legs in the non-EOA and EOA groups, respectively ( Table 1 ). There were no significant differences between the groups in terms of sex distribution ( p = 0.22), age ( p = 0.46), height ( p = 0.07), weight ( p = 0.78), BMI ( p = ... | peerj | yes | Change the cell values | papers/dev/peerj_19471.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0269 | tables/dev/val_tab_0429.html | |
19471 | val_tab_0430 | Tukey’s post-hoc analysis revealed that those in C2 had the youngest age (44.80 ± 11.30 years, p < 0.001 vs. all other clusters) and highest BMI (25.59 ± 2.42, p < 0.001 vs. all other clusters). | Supported | Table 2: Comparisons of features between the clusters classified by Louvain clustering unsupervised machine learning. | table | tables_png/dev/val_tab_0430.png | To validate the quality and distinctiveness of the Louvain clustering solution, we calculated widely-used cluster validity indices. The Davies–Bouldin index was 2.09, and the Calinski–Harabasz index was 59.26. While the Davies–Bouldin index was somewhat high (lower values indicate better separation), the Calinski–Harab... | peerj | no | Change the cell values | papers/dev/peerj_19471.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0270 | tables/dev/val_tab_0430.html | |
19471 | val_tab_0431 | C2 included exclusively males, whereas the other clusters had a mixed sex composition. | Supported | Table 2: Comparisons of features between the clusters classified by Louvain clustering unsupervised machine learning. | table | tables_png/dev/val_tab_0431.png | To validate the quality and distinctiveness of the Louvain clustering solution, we calculated widely-used cluster validity indices. The Davies–Bouldin index was 2.09, and the Calinski–Harabasz index was 59.26. While the Davies–Bouldin index was somewhat high (lower values indicate better separation), the Calinski–Harab... | peerj | no | Change the cell values | papers/dev/peerj_19471.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0271 | tables/dev/val_tab_0431.html | |
19471 | val_tab_0432 | C2 included exclusively males, whereas the other clusters had a mixed sex composition. | Refuted | Table 2: Comparisons of features between the clusters classified by Louvain clustering unsupervised machine learning. | table | tables_png/dev/val_tab_0432.png | To validate the quality and distinctiveness of the Louvain clustering solution, we calculated widely-used cluster validity indices. The Davies–Bouldin index was 2.09, and the Calinski–Harabasz index was 59.26. While the Davies–Bouldin index was somewhat high (lower values indicate better separation), the Calinski–Harab... | peerj | no | Change the cell values | papers/dev/peerj_19471.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0271 | tables/dev/val_tab_0432.html | |
19459 | val_tab_0434 | Participants with longer sleep latency had a higher percentage of suboptimal SRH, evening-type chronotype, and slept less than 6 h per night compared to those with sleep latency of less than 10 min ( P < 0.05). | Supported | Table 1: Characteristics of study participants. | table | tables_png/dev/val_tab_0434.png | The study included 1,396 participants, with a mean age of 19.6 ±1.2 years, and 58.6% female, of whom 599 (42.9%) reported suboptimal SRH and 390 (27.9%) reported prolonged sleep latency (≥30 min). Table 1 presents the basic characteristics of the participants. | peerj | no | Change the cell values | papers/dev/peerj_19459.json | CC BY-NC 4.0 | https://creativecommons.org/licenses/by-nc/4.0/ | 0272 | tables/dev/val_tab_0434.html | |
19459 | val_tab_0435 | However, there were no significant differences in gender, residence, single child, parental education, BMI, afternoon nap, sitting time, and physical activity among these groups. | Supported | Table 1: Characteristics of study participants. | table | tables_png/dev/val_tab_0435.png | The study included 1,396 participants, with a mean age of 19.6 ±1.2 years, and 58.6% female, of whom 599 (42.9%) reported suboptimal SRH and 390 (27.9%) reported prolonged sleep latency (≥30 min). Table 1 presents the basic characteristics of the participants. Participants with longer sleep latency had a higher percent... | peerj | yes | Change the cell values | papers/dev/peerj_19459.json | CC BY-NC 4.0 | https://creativecommons.org/licenses/by-nc/4.0/ | 0273 | tables/dev/val_tab_0435.html | |
19459 | val_tab_0436 | Compared to participants with sleep latency of less than 10 min, those with longer sleep latency demonstrated an increased risk of suboptimal SRH. | Supported | Table 2: Association between sleep latency and odds ratios (95% CIs) of suboptimal self-rated health among medical students. | table | tables_png/dev/val_tab_0436.png | Table 2 presents the multivariable-adjusted ORs of suboptimal SRH associated with sleep latency. | peerj | no | Change the cell values | papers/dev/peerj_19459.json | CC BY-NC 4.0 | https://creativecommons.org/licenses/by-nc/4.0/ | 0274 | tables/dev/val_tab_0436.html | |
17090 | val_tab_0437 | As shown in Table 1 , there are significant differences in the mean of AA scores across grade levels and BMI index (F = 3.402 P = 0.009; F = 5.752 P = 0.003). | Supported | Table 1: Differences in appearance anxiety, interpersonal sensitivity, social support, and depression under different subgroups of basic demographic characteristics. | table | tables_png/dev/val_tab_0437.png | All participants were asked to complete a questionnaire that included gender, age, grade, height, weight, whether they were an only child, whether their home address was town or village, monthly household income, GPA for the previous school year, and disposable income. Specific issues and analysis results are shown in ... | peerj | other sources | Change the cell values | papers/dev/peerj_17090.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0275 | tables/dev/val_tab_0437.html | |
17090 | val_tab_0438 | As shown in Table 1 , there are significant differences in the mean of AA scores across grade levels and BMI index (F = 3.402 P = 0.009; F = 5.752 P = 0.003). | Refuted | Table 1: Differences in appearance anxiety, interpersonal sensitivity, social support, and depression under different subgroups of basic demographic characteristics. | table | tables_png/dev/val_tab_0438.png | All participants were asked to complete a questionnaire that included gender, age, grade, height, weight, whether they were an only child, whether their home address was town or village, monthly household income, GPA for the previous school year, and disposable income. Specific issues and analysis results are shown in ... | peerj | other sources | Change the cell values | papers/dev/peerj_17090.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0275 | tables/dev/val_tab_0438.html | |
17090 | val_tab_0440 | Results of the analysis ( Table 3 ) showed that AA was positively correlated with depression (Index = 0.168, P < 0.001). | Supported | Table 3: Regression analysis of appearance anxiety, interpersonal sensitivity, social support, and depression. | table | tables_png/dev/val_tab_0440.png | A multiple mediation analysis was conducted to explore the mediation effects of IS and SS in a college student population. Control variables included gender, age, BMI, GPA, being an only child or not, and home address. AA and depression were entered as independent and dependent variables respectively. The proposed medi... | peerj | other sources | Change the cell values | papers/dev/peerj_17090.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0276 | tables/dev/val_tab_0440.html | |
17090 | val_tab_0441 | Secondly, AA was positively correlated with IS (Index = 0.575, P < 0.001) and negatively correlated with SS (Index = −0.234, P < 0.001). | Supported | Table 3: Regression analysis of appearance anxiety, interpersonal sensitivity, social support, and depression. | table | tables_png/dev/val_tab_0441.png | A multiple mediation analysis was conducted to explore the mediation effects of IS and SS in a college student population. Control variables included gender, age, BMI, GPA, being an only child or not, and home address. AA and depression were entered as independent and dependent variables respectively. The proposed medi... | peerj | other sources | Change the cell values | papers/dev/peerj_17090.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0277 | tables/dev/val_tab_0441.html | |
17090 | val_tab_0442 | Secondly, AA was positively correlated with IS (Index = 0.575, P < 0.001) and negatively correlated with SS (Index = −0.234, P < 0.001). | Refuted | Table 3: Regression analysis of appearance anxiety, interpersonal sensitivity, social support, and depression. | table | tables_png/dev/val_tab_0442.png | A multiple mediation analysis was conducted to explore the mediation effects of IS and SS in a college student population. Control variables included gender, age, BMI, GPA, being an only child or not, and home address. AA and depression were entered as independent and dependent variables respectively. The proposed medi... | peerj | other sources | Change the cell values | papers/dev/peerj_17090.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0277 | tables/dev/val_tab_0442.html | |
17090 | val_tab_0443 | IS was negatively correlated with SS (Index = −0.179, P < 0.001). | Supported | Table 3: Regression analysis of appearance anxiety, interpersonal sensitivity, social support, and depression. | table | tables_png/dev/val_tab_0443.png | A multiple mediation analysis was conducted to explore the mediation effects of IS and SS in a college student population. Control variables included gender, age, BMI, GPA, being an only child or not, and home address. AA and depression were entered as independent and dependent variables respectively. The proposed medi... | peerj | other sources | Change the cell values | papers/dev/peerj_17090.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0278 | tables/dev/val_tab_0443.html | |
17295 | val_tab_0445 | There were nine male and nine female participants in each group ( Table 1 ). | Supported | Table 1: Demographics of the participants in each group (mean ± SD). | table | tables_png/dev/val_tab_0445.png | Fifty-four participants were recruited by advertisement from universities in Taipei. This sample size was determined based on an a priori power analysis (alpha = 0.05, power = 0.80), and the effect size, f = 0.44 ( Cohen, 1992 ), and similar to that seen in ERP studies investigating visuospatial attention and working m... | peerj | no | Change the cell values | papers/dev/peerj_17295.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0279 | tables/dev/val_tab_0445.html | |
17295 | val_tab_0446 | There were nine male and nine female participants in each group ( Table 1 ). | Refuted | Table 1: Demographics of the participants in each group (mean ± SD). | table | tables_png/dev/val_tab_0446.png | Fifty-four participants were recruited by advertisement from universities in Taipei. This sample size was determined based on an a priori power analysis (alpha = 0.05, power = 0.80), and the effect size, f = 0.44 ( Cohen, 1992 ), and similar to that seen in ERP studies investigating visuospatial attention and working m... | peerj | no | Change the cell values | papers/dev/peerj_17295.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0279 | tables/dev/val_tab_0446.html | |
17295 | val_tab_0447 | There was a significant difference in IQ ( F (2, 51) = 9.061, p < 0.01, η 2 = 0.262), and a post hoc comparison revealed that ETT players had a lower IQ than ATT players ( p < 0.003) and nonathletes ( p < 0.001), but no significant difference between the ATT and nonathletes ( p < 0.368) was observed. | Supported | Table 1: Demographics of the participants in each group (mean ± SD). | table | tables_png/dev/val_tab_0447.png | Table 1 presents the participants’ characteristics. No significant differences were observed in age ( F (2, 51) = 2.863, p = 0.066), height ( F (2, 51) = 0.420, p = 0.659), weight ( F (2, 51) = 0.556, p = 0.577), handedness scores ( F (2, 51) = 0.536, p = 0.589), video game experience ( F (2, 51) = 1.387, p = 0.259), a... | peerj | no | Change the cell values | papers/dev/peerj_17295.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0280 | tables/dev/val_tab_0447.html | |
17295 | val_tab_0448 | There was a significant difference in IQ ( F (2, 51) = 9.061, p < 0.01, η 2 = 0.262), and a post hoc comparison revealed that ETT players had a lower IQ than ATT players ( p < 0.003) and nonathletes ( p < 0.001), but no significant difference between the ATT and nonathletes ( p < 0.368) was observed. | Refuted | Table 1: Demographics of the participants in each group (mean ± SD). | table | tables_png/dev/val_tab_0448.png | Table 1 presents the participants’ characteristics. No significant differences were observed in age ( F (2, 51) = 2.863, p = 0.066), height ( F (2, 51) = 0.420, p = 0.659), weight ( F (2, 51) = 0.556, p = 0.577), handedness scores ( F (2, 51) = 0.536, p = 0.589), video game experience ( F (2, 51) = 1.387, p = 0.259), a... | peerj | no | Change the cell values | papers/dev/peerj_17295.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0280 | tables/dev/val_tab_0448.html | |
16988 | val_tab_0449 | A total of 54 COPD patients (25 in stages I–II and 29 in stages III–IV) and 24 healthy individuals participated in the study from September 9, 2021, to May 1, 2022. | Supported | Table 1: Participants’ clinical characteristics and demographics. | table | tables_png/dev/val_tab_0449.png | peerj | no | Change the cell values | papers/dev/peerj_16988.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0281 | tables/dev/val_tab_0449.html | ||
17284 | val_tab_0451 | 36 pregnant women were advised to carefully consider whether to continue with the termination, and 229 pregnant women were advised to continue with pregnancy follow-up ( Table 1 ). | Supported | Table 1: Comparison of pregnancy outcomes in pregnant women with different characteristics of fetal abnormalities. | table | tables_png/dev/val_tab_0451.png | A total of 398 pregnant women visited our hospital between May 2022 and March 2023. Among them, 273 had a birth outcome (delivery or termination of pregnancy). However, three participants did not answer the phone during the telephone follow-up and five refused to participate in the study. Ultimately, 265 pregnant women... | peerj | no | Change the cell values | papers/dev/peerj_17284.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0282 | tables/dev/val_tab_0451.html | |
17284 | val_tab_0452 | 36 pregnant women were advised to carefully consider whether to continue with the termination, and 229 pregnant women were advised to continue with pregnancy follow-up ( Table 1 ). | Refuted | Table 1: Comparison of pregnancy outcomes in pregnant women with different characteristics of fetal abnormalities. | table | tables_png/dev/val_tab_0452.png | A total of 398 pregnant women visited our hospital between May 2022 and March 2023. Among them, 273 had a birth outcome (delivery or termination of pregnancy). However, three participants did not answer the phone during the telephone follow-up and five refused to participate in the study. Ultimately, 265 pregnant women... | peerj | no | Change the cell values | papers/dev/peerj_17284.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0282 | tables/dev/val_tab_0452.html | |
17284 | val_tab_0453 | Chi-square test analysis showed significant differences in the gestational age at diagnosis of fetal abnormalities, the number of fetal abnormalities, diagnostic gestational age, and treatment recommendations of doctors among pregnant women ( P < 0.05). | Supported | Table 1: Comparison of pregnancy outcomes in pregnant women with different characteristics of fetal abnormalities. | table | tables_png/dev/val_tab_0453.png | peerj | no | Change the cell values | papers/dev/peerj_17284.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0283 | tables/dev/val_tab_0453.html | ||
17284 | val_tab_0454 | However, there were no significant differences in maternal age, number of births, abortion history, history of fetal abnormalities, mode of conception, pregnancy complications, education level, employment status, or place of abode ( Table 1 ). | Supported | Table 1: Comparison of pregnancy outcomes in pregnant women with different characteristics of fetal abnormalities. | table | tables_png/dev/val_tab_0454.png | Chi-square test analysis showed significant differences in the gestational age at diagnosis of fetal abnormalities, the number of fetal abnormalities, diagnostic gestational age, and treatment recommendations of doctors among pregnant women ( P < 0.05). | peerj | no | Change the cell values | papers/dev/peerj_17284.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0284 | tables/dev/val_tab_0454.html | |
17284 | val_tab_0455 | However, there were no significant differences in maternal age, number of births, abortion history, history of fetal abnormalities, mode of conception, pregnancy complications, education level, employment status, or place of abode ( Table 1 ). | Refuted | Table 1: Comparison of pregnancy outcomes in pregnant women with different characteristics of fetal abnormalities. | table | tables_png/dev/val_tab_0455.png | Chi-square test analysis showed significant differences in the gestational age at diagnosis of fetal abnormalities, the number of fetal abnormalities, diagnostic gestational age, and treatment recommendations of doctors among pregnant women ( P < 0.05). | peerj | no | Change the cell values | papers/dev/peerj_17284.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0284 | tables/dev/val_tab_0455.html | |
17284 | val_tab_0456 | Pregnant women with multiple fetal abnormalities were 3.774 times more likely to undergo labor induction than those with a single fetal abnormality (OR = 3.774, 95% CI [1.640–8.683]). | Supported | Table 2: Analysis of influencing factors of pregnancy outcome in pregnant women with fetal abnormalities. | table | tables_png/dev/val_tab_0456.png | peerj | no | Change the cell values | papers/dev/peerj_17284.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0285 | tables/dev/val_tab_0456.html | ||
17284 | val_tab_0457 | Pregnant women who were advised to terminate their pregnancies or carefully consider whether to continue gestation were 41.113 times more likely to undergo labor induction compared to those who were advised to continue the pregnancy (OR = 41.113, 95% CI [11.028–153.267]), ( Table 2 ). | Supported | Table 2: Analysis of influencing factors of pregnancy outcome in pregnant women with fetal abnormalities. | table | tables_png/dev/val_tab_0457.png | Pregnant women with multiple fetal abnormalities were 3.774 times more likely to undergo labor induction than those with a single fetal abnormality (OR = 3.774, 95% CI [1.640–8.683]). | peerj | no | Change the cell values | papers/dev/peerj_17284.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0286 | tables/dev/val_tab_0457.html | |
17403 | val_tab_0460 | A day effect was only found for the Nine Hole Peg test, revealing that the time for completing the task during the assessment-2 was less than during the assessment-1 (MD: 0.39 s; 95% CI [0.19–0.60]; P < 0.001). | Supported | Table 2: Clinical characteristics of participants in the different test for the wrist extensor muscles. | table | tables_png/dev/val_tab_0460.png | No side * day interaction was found in the RM-ANOVA for any of the variables ( P > 0.114), indicating a similar pattern across assessments of the dominant and non-dominant sides. | peerj | no | Change the cell values | papers/dev/peerj_17403.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0287 | tables/dev/val_tab_0460.html | |
17403 | val_tab_0462 | For the active range of motion, reliability was ‘excellent’ for both extension, flexion, and total range (ICC: 0.94–0.97). | Supported | Table 3: Reliability indicators of myotonometry, manual dexterity, pressure pain thresholds, active range of motion, and maximal isometric strength for the wrist extensor muscles. | table | tables_png/dev/val_tab_0462.png | Table 3 presents ICC 3,1 , SEM, MDC 90 , and MDC 95 for each variable. For the muscle mechanical properties of the dominant and non-dominant sides, reliability was ‘excellent’ for the frequency and stiffness parameters (ICC: 0.91–0.96), and ‘good’ to ‘excellent’ for the decrement (ICC: 0.86–0.91). For the manual dexter... | peerj | no | Change the cell values | papers/dev/peerj_17403.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0288 | tables/dev/val_tab_0462.html | |
17403 | val_tab_0463 | Similarly, reliability was ‘excellent’ for the maximal isometric strength on both sides (ICC: 0.97). | Supported | Table 3: Reliability indicators of myotonometry, manual dexterity, pressure pain thresholds, active range of motion, and maximal isometric strength for the wrist extensor muscles. | table | tables_png/dev/val_tab_0463.png | Table 3 presents ICC 3,1 , SEM, MDC 90 , and MDC 95 for each variable. For the muscle mechanical properties of the dominant and non-dominant sides, reliability was ‘excellent’ for the frequency and stiffness parameters (ICC: 0.91–0.96), and ‘good’ to ‘excellent’ for the decrement (ICC: 0.86–0.91). For the manual dexter... | peerj | no | Change the cell values | papers/dev/peerj_17403.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0289 | tables/dev/val_tab_0463.html | |
17403 | val_tab_0464 | Similarly, reliability was ‘excellent’ for the maximal isometric strength on both sides (ICC: 0.97). | Refuted | Table 3: Reliability indicators of myotonometry, manual dexterity, pressure pain thresholds, active range of motion, and maximal isometric strength for the wrist extensor muscles. | table | tables_png/dev/val_tab_0464.png | Table 3 presents ICC 3,1 , SEM, MDC 90 , and MDC 95 for each variable. For the muscle mechanical properties of the dominant and non-dominant sides, reliability was ‘excellent’ for the frequency and stiffness parameters (ICC: 0.91–0.96), and ‘good’ to ‘excellent’ for the decrement (ICC: 0.86–0.91). For the manual dexter... | peerj | no | Change the cell values | papers/dev/peerj_17403.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0289 | tables/dev/val_tab_0464.html | |
17403 | val_tab_0465 | In the case of the asymmetries, reliability was ‘excellent’ for the active range of motion in flexion (ICC: 0.96); ‘good’ for the myotonometry parameters (ICC: 0.72–0.78), the Nine-Hole Peg test scoring (ICC: 0.72), the total and extension active range of motion (ICC: 0.73–0.89), and the maximal isometric strength (ICC... | Supported | Table 3: Reliability indicators of myotonometry, manual dexterity, pressure pain thresholds, active range of motion, and maximal isometric strength for the wrist extensor muscles. | table | tables_png/dev/val_tab_0465.png | Table 3 presents ICC 3,1 , SEM, MDC 90 , and MDC 95 for each variable. For the muscle mechanical properties of the dominant and non-dominant sides, reliability was ‘excellent’ for the frequency and stiffness parameters (ICC: 0.91–0.96), and ‘good’ to ‘excellent’ for the decrement (ICC: 0.86–0.91). For the manual dexter... | peerj | no | Change the cell values | papers/dev/peerj_17403.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0290 | tables/dev/val_tab_0465.html | |
17403 | val_tab_0466 | In the case of the asymmetries, reliability was ‘excellent’ for the active range of motion in flexion (ICC: 0.96); ‘good’ for the myotonometry parameters (ICC: 0.72–0.78), the Nine-Hole Peg test scoring (ICC: 0.72), the total and extension active range of motion (ICC: 0.73–0.89), and the maximal isometric strength (ICC... | Refuted | Table 3: Reliability indicators of myotonometry, manual dexterity, pressure pain thresholds, active range of motion, and maximal isometric strength for the wrist extensor muscles. | table | tables_png/dev/val_tab_0466.png | Table 3 presents ICC 3,1 , SEM, MDC 90 , and MDC 95 for each variable. For the muscle mechanical properties of the dominant and non-dominant sides, reliability was ‘excellent’ for the frequency and stiffness parameters (ICC: 0.91–0.96), and ‘good’ to ‘excellent’ for the decrement (ICC: 0.86–0.91). For the manual dexter... | peerj | no | Change the cell values | papers/dev/peerj_17403.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0290 | tables/dev/val_tab_0466.html | |
17407 | val_tab_0468 | In addition to plastic, various other types of anthropogenic materials found in 23 out of 1,447 pellets were identified and classified as non-plastic debris. | Supported | Table 2: Composition of plastic and non-plastic debris found in 1,447 analyzed pellets of the neotropic cormorant,N. brasilianus, on the Circuito de Playas Costa Verde (CPCV), Lima, Perú, during both the pre-pandemic and pandemic phases. | table | tables_png/dev/val_tab_0468.png | peerj | no | Change the cell values | papers/dev/peerj_17407.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0291 | tables/dev/val_tab_0468.html | ||
17407 | val_tab_0469 | The most encountered category was fibers, followed by metal, glass, wood, paint chips and balloons ( Table 2 ). | Supported | Table 2: Composition of plastic and non-plastic debris found in 1,447 analyzed pellets of the neotropic cormorant,N. brasilianus, on the Circuito de Playas Costa Verde (CPCV), Lima, Perú, during both the pre-pandemic and pandemic phases. | table | tables_png/dev/val_tab_0469.png | In addition to plastic, various other types of anthropogenic materials found in 23 out of 1,447 pellets were identified and classified as non-plastic debris. | peerj | yes | Change the cell values | papers/dev/peerj_17407.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0292 | tables/dev/val_tab_0469.html | |
18000 | val_tab_0472 | Among the nine independent variables, five correlated with arm swing speed at BI ( Table 1 ). | Supported | Table 1: Peak angular momentum variables and correlations with the arm swing speed at ball impact (BI). | table | tables_png/dev/val_tab_0472.png | peerj | no | Change the cell values | papers/dev/peerj_18000.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0293 | tables/dev/val_tab_0472.html | ||
18000 | val_tab_0473 | Our results indicate significant correlations between the peak angular momentums of the attack arm, non-attack arm, non-attack leg, forearm, and hand with the arm swing speed at BI ( Table 1 ). | Supported | Table 1: Peak angular momentum variables and correlations with the arm swing speed at ball impact (BI). | table | tables_png/dev/val_tab_0473.png | This study investigated the relationships between angular momentum variables and swing hand speed during jump serve aerial spiking, utilising correlation and regression analyses. | peerj | no | Change the cell values | papers/dev/peerj_18000.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0294 | tables/dev/val_tab_0473.html | |
18000 | val_tab_0474 | The peak angular momentum of the non-attack leg (−0.026 ± 0.012 s –1 ) exhibited negative correlations with the arm swing speed at BI ( Table 1 ). | Supported | Table 1: Peak angular momentum variables and correlations with the arm swing speed at ball impact (BI). | table | tables_png/dev/val_tab_0474.png | In the later part of the arm-cocking phase, the angular momentums of the attack and non-attack legs simultaneously rotated clockwise (−). | peerj | no | Change the cell values | papers/dev/peerj_18000.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0295 | tables/dev/val_tab_0474.html | |
18000 | val_tab_0475 | The peak angular momentum of the non-attack leg (−0.026 ± 0.012 s –1 ) exhibited negative correlations with the arm swing speed at BI ( Table 1 ). | Refuted | Table 1: Peak angular momentum variables and correlations with the arm swing speed at ball impact (BI). | table | tables_png/dev/val_tab_0475.png | In the later part of the arm-cocking phase, the angular momentums of the attack and non-attack legs simultaneously rotated clockwise (−). | peerj | no | Change the cell values | papers/dev/peerj_18000.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0295 | tables/dev/val_tab_0475.html | |
18589 | val_tab_0479 | There was no significant difference in BMD between the two groups ( P > 0.05). | Supported | Table 4: Comparison of two groups of BMD and abnormal rates. | table | tables_png/dev/val_tab_0479.png | peerj | no | Change the cell values | papers/dev/peerj_18589.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0296 | tables/dev/val_tab_0479.html | ||
18589 | val_tab_0480 | There was no significant difference in BMD between the two groups ( P > 0.05). | Refuted | Table 4: Comparison of two groups of BMD and abnormal rates. | table | tables_png/dev/val_tab_0480.png | peerj | no | Change the cell values | papers/dev/peerj_18589.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0296 | tables/dev/val_tab_0480.html | ||
18589 | val_tab_0481 | There was no difference in the prevalence of osteoporosis and low bone mass rate between the two groups, indicating that there was no significant difference in BMD between the two groups ( Table 4 ). | Supported | Table 4: Comparison of two groups of BMD and abnormal rates. | table | tables_png/dev/val_tab_0481.png | There was no significant difference in BMD between the two groups ( P > 0.05). | peerj | no | Change the cell values | papers/dev/peerj_18589.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0297 | tables/dev/val_tab_0481.html | |
18589 | val_tab_0482 | There was no difference in the prevalence of osteoporosis and low bone mass rate between the two groups, indicating that there was no significant difference in BMD between the two groups ( Table 4 ). | Refuted | Table 4: Comparison of two groups of BMD and abnormal rates. | table | tables_png/dev/val_tab_0482.png | There was no significant difference in BMD between the two groups ( P > 0.05). | peerj | no | Change the cell values | papers/dev/peerj_18589.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0297 | tables/dev/val_tab_0482.html | |
18596 | val_tab_0483 | Additionally, 46.6% of participants were primary school graduates, 9.4% reported alcohol use, and 13% were smokers, with an average duration of smoking of 22.83 ± 6.94 years. | Supported | Table 1: Participants’ personal data, IIEF total score (n= 223). | table | tables_png/dev/val_tab_0483.png | Table 1 presents the personal data of the participants along with the total score of IIEF. The participants had a mean age of 63.26 ± 7.62 years, with 97.3% being married. | peerj | no | Change the cell values | papers/dev/peerj_18596.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0298 | tables/dev/val_tab_0483.html | |
18596 | val_tab_0484 | Additionally, 46.6% of participants were primary school graduates, 9.4% reported alcohol use, and 13% were smokers, with an average duration of smoking of 22.83 ± 6.94 years. | Refuted | Table 1: Participants’ personal data, IIEF total score (n= 223). | table | tables_png/dev/val_tab_0484.png | Table 1 presents the personal data of the participants along with the total score of IIEF. The participants had a mean age of 63.26 ± 7.62 years, with 97.3% being married. | peerj | no | Change the cell values | papers/dev/peerj_18596.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0298 | tables/dev/val_tab_0484.html | |
18596 | val_tab_0485 | The mean duration of hypertension was 7.92 ± 4.06 years, and the most commonly used medication among participants (27.8%) was ACE Inhibitors. | Supported | Table 1: Participants’ personal data, IIEF total score (n= 223). | table | tables_png/dev/val_tab_0485.png | Table 1 presents the personal data of the participants along with the total score of IIEF. The participants had a mean age of 63.26 ± 7.62 years, with 97.3% being married. Additionally, 46.6% of participants were primary school graduates, 9.4% reported alcohol use, and 13% were smokers, with an average duration of smok... | peerj | no | Change the cell values | papers/dev/peerj_18596.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0299 | tables/dev/val_tab_0485.html | |
18596 | val_tab_0486 | Notably, 81.6% of participants had erectile dysfunction, with the mean IIEF total score indicating a mild level of dysfunction (18.72 ± 3.60) ( Table 1 ). | Supported | Table 1: Participants’ personal data, IIEF total score (n= 223). | table | tables_png/dev/val_tab_0486.png | Table 1 presents the personal data of the participants along with the total score of IIEF. The participants had a mean age of 63.26 ± 7.62 years, with 97.3% being married. Additionally, 46.6% of participants were primary school graduates, 9.4% reported alcohol use, and 13% were smokers, with an average duration of smok... | peerj | no | Change the cell values | papers/dev/peerj_18596.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0300 | tables/dev/val_tab_0486.html | |
18596 | val_tab_0487 | Notably, 81.6% of participants had erectile dysfunction, with the mean IIEF total score indicating a mild level of dysfunction (18.72 ± 3.60) ( Table 1 ). | Refuted | Table 1: Participants’ personal data, IIEF total score (n= 223). | table | tables_png/dev/val_tab_0487.png | Table 1 presents the personal data of the participants along with the total score of IIEF. The participants had a mean age of 63.26 ± 7.62 years, with 97.3% being married. Additionally, 46.6% of participants were primary school graduates, 9.4% reported alcohol use, and 13% were smokers, with an average duration of smok... | peerj | no | Change the cell values | papers/dev/peerj_18596.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0300 | tables/dev/val_tab_0487.html | |
18596 | val_tab_0488 | Significant negative correlations were observed between age and the IIEF total score, as well as between the duration of hypertension and the IIEF total score ( p < 0.05). | Supported | Table 3: Correlation between age, duration of hypertension, duration of smoking and IIEF total. | table | tables_png/dev/val_tab_0488.png | Table 3 displays the correlation between age, duration of hypertension, duration of smoking, and the IIEF total score. | peerj | no | Change the cell values | papers/dev/peerj_18596.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0301 | tables/dev/val_tab_0488.html | |
17773 | val_tab_0489 | Regarding methodological quality assessment tools, 12 studies utilized the Cochrane Collaboration Network RoB risk assessment tool, four employed the PEDro scale, one used the Cochrane Collaboration Network RoB and PEDro, and one applied the EPHPP for risk assessment. | Supported | Table 1: Basic characteristics of the literature included in the study. | table | tables_png/dev/val_tab_0489.png | The included meta-analyses were published between 2018 and 2023, and all the original studies were randomized and controlled. The largest study included 2,526 participants and the smallest had 182 participants, involving a total of 18,461 individuals. The interventions in the trial groups consisted of aerobic, physical... | peerj | no | Change the cell values | papers/dev/peerj_17773.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0302 | tables/dev/val_tab_0489.html | |
18897 | val_tab_0490 | However, percentage of population that were married had statistically significant low diabetes-related ED visit rate ( Table 5 ). | Supported | Table 5: Results of a multivariable negative binomial regression model examining the associations between county characteristics and the rate of diabetes-related emergency department visits in Florida, 2019. | table | tables_png/dev/val_tab_0490.png | Based on the final multivariable negative binomial model, the following independent variables had significant high county-level non-gestational diabetes-related ED visit rates: percentages of population who were non-Hispanic Black, current smokers, had diabetes, and had no insurance coverage ( Table 5 ). | peerj | no | Change the cell values | papers/dev/peerj_18897.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0303 | tables/dev/val_tab_0490.html | |
18897 | val_tab_0491 | However, percentage of population that were married had statistically significant low diabetes-related ED visit rate ( Table 5 ). | Refuted | Table 5: Results of a multivariable negative binomial regression model examining the associations between county characteristics and the rate of diabetes-related emergency department visits in Florida, 2019. | table | tables_png/dev/val_tab_0491.png | Based on the final multivariable negative binomial model, the following independent variables had significant high county-level non-gestational diabetes-related ED visit rates: percentages of population who were non-Hispanic Black, current smokers, had diabetes, and had no insurance coverage ( Table 5 ). | peerj | no | Change the cell values | papers/dev/peerj_18897.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0303 | tables/dev/val_tab_0491.html | |
17017 | val_tab_0492 | A comparative analysis of hematology parameters indicated that children exposed to lead had significantly lower values of monocytes, basophil, hemoglobin, red blood cell count (RBC), hematocrit (HCT), MCH, MCHC, mean platelet volume (MPV), and platelet volume distribution width (PDW), and higher white blood cell count ... | Supported | Table 1: Hematologic parameters of the lead-poisoned children and the healthy controls. | table | tables_png/dev/val_tab_0492.png | peerj | no | Change the cell values | papers/dev/peerj_17017.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0304 | tables/dev/val_tab_0492.html | ||
17017 | val_tab_0493 | A comparative analysis of hematology parameters indicated that children exposed to lead had significantly lower values of monocytes, basophil, hemoglobin, red blood cell count (RBC), hematocrit (HCT), MCH, MCHC, mean platelet volume (MPV), and platelet volume distribution width (PDW), and higher white blood cell count ... | Refuted | Table 1: Hematologic parameters of the lead-poisoned children and the healthy controls. | table | tables_png/dev/val_tab_0493.png | peerj | no | Change the cell values | papers/dev/peerj_17017.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0304 | tables/dev/val_tab_0493.html | ||
19471 | val_tab_0495 | For SU kinematics, C4 exhibited the largest horizontal displacements for the pelvis, femur, knee, and lower leg, all significantly higher than other clusters ( p < 0.001). | Supported | Table 2: Comparisons of features between the clusters classified by Louvain clustering unsupervised machine learning. | table | tables_png/dev/val_tab_0495.png | To validate the quality and distinctiveness of the Louvain clustering solution, we calculated widely-used cluster validity indices. The Davies–Bouldin index was 2.09, and the Calinski–Harabasz index was 59.26. While the Davies–Bouldin index was somewhat high (lower values indicate better separation), the Calinski–Harab... | peerj | other sources | Change the cell values | papers/dev/peerj_19471.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0305 | tables/dev/val_tab_0495.html | |
19471 | val_tab_0496 | C1 exhibited negative mean values for the knee and lower leg displacements during SD, significantly different from other clusters ( p < 0.001). | Supported | Table 2: Comparisons of features between the clusters classified by Louvain clustering unsupervised machine learning. | table | tables_png/dev/val_tab_0496.png | To validate the quality and distinctiveness of the Louvain clustering solution, we calculated widely-used cluster validity indices. The Davies–Bouldin index was 2.09, and the Calinski–Harabasz index was 59.26. While the Davies–Bouldin index was somewhat high (lower values indicate better separation), the Calinski–Harab... | peerj | other sources | Change the cell values | papers/dev/peerj_19471.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0306 | tables/dev/val_tab_0496.html | |
17447 | val_tab_0497 | The baseline characteristics were described in Table 1 . For women <35 years old, the median age for the HRT group and GnRH-a group were 30.6 and 31.3 years, respectively. | Supported | Table 1: Demographic characteristics of patients between different age groups and endometrial preparation groups. | table | tables_png/dev/val_tab_0497.png | peerj | other sources | Change the cell values | papers/dev/peerj_17447.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0307 | tables/dev/val_tab_0497.html | ||
17447 | val_tab_0498 | The baseline characteristics were described in Table 1 . For women <35 years old, the median age for the HRT group and GnRH-a group were 30.6 and 31.3 years, respectively. | Refuted | Table 1: Demographic characteristics of patients between different age groups and endometrial preparation groups. | table | tables_png/dev/val_tab_0498.png | peerj | other sources | Change the cell values | papers/dev/peerj_17447.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0307 | tables/dev/val_tab_0498.html | ||
17447 | val_tab_0499 | Women aged <35 years had higher clinical pregnancy and live birth rates than women aged ≥35 years. | Supported | Table 2: Comparison of reproductive outcomes. | table | tables_png/dev/val_tab_0499.png | Table 2 shows the clinical outcomes of FET. | peerj | other sources | Change the cell values | papers/dev/peerj_17447.json | CC BY 4.0 | https://creativecommons.org/licenses/by/4.0/ | 0308 | tables/dev/val_tab_0499.html |
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