checks.md

#	Paper	Strategy Used
1	Tan et al. (2022) — Sentiment analysis with ensemble hybrid deep learning model, IEEE Access	A2: XGB+LGB Prediction Averaging
2	Bakasa & Viriri (2023) — Stacked ensemble deep learning for pancreas cancer classification, Frontiers in AI	A3: 2-Layer Stacking (LGB+XGB → Ridge meta)
3	Kumari & Toshniwal (2021) — XGBoost forest and DNN ensemble for solar irradiance forecasting, Journal of Cleaner Production	A4: Weighted Vote + Phase B RTGB inspiration
4	Emami & Martínez-Muñoz (2023) — A gradient boosting approach for training CNNs and DNNs, IEEE Open Journal of Signal Processing	A5: Sequential Residual Boosting
5	Badirli et al. (2020) — GrowNet: Gradient boosting neural networks, arXiv	A6: Feature-Split Specialist Ensemble
6	Thanka et al. (2023) — Hybrid approach for melanoma classification using VGG16 + LightGBM, Computer Methods and Programs in Biomedicine	Phase B: LightGBM as final classifier stage
7	Almulihi et al. (2022) — Ensemble learning based on CNN-LSTM and CNN-GRU hybrid, Diagnostics	Phase B: LSTM architecture design
8	Sharmin et al. (2023) — Hybrid ResNet50V2 + LightGBM for breast cancer detection, IEEE Access	Phase B: LGB+LSTM role split concept

#	Gap in Existing Research	How Our RTGB Fills It
1	Most hybrids use fixed blend weights (e.g. simple average, soft voting) — same α for every sample	RTGB uses a per-sample learned gate α — the GateNet decides blend ratio dynamically based on input context
2	LSTM and LGB are trained independently on the same target — no information flows between them	RTGB's LSTM is trained on LGB's residuals, not raw targets — forced specialisation, explicit knowledge transfer
3	Stacking meta-learners (Ridge, XGB) take predictions as input only — ignore temporal context	GateNet takes predictions plus tabular context features — the gate is context-aware, not just prediction-aware
4	Most LGB+LSTM papers (wind, solar) do parallel training then late fusion	RTGB is sequential: LGB runs first, LSTM sees what LGB failed at — asymmetric roles by design
5	Residual boosting exists (GrowNet, GB-DNN) but uses homogeneous weak learners (all NNs or all trees)	RTGB mixes heterogeneous paradigms — gradient boosting for global structure, LSTM for temporal error patterns
6	Gate/attention mechanisms in hybrids are applied to features or sequence steps, not to model predictions	RTGB's gate operates at the prediction level — it is a learned meta-controller over two specialist outputs
7	Ablation studies rarely isolate the contribution of the blending layer	RTGB has built-in ablation: LGB-only, LGB+LSTM-corrected, and full RTGB are all logged — contribution of each stage is measurable
8	No wind forecasting paper combines residual transfer + adaptive gating in a single end-to-end pipeline	RTGB is the first tabular wind forecasting architecture to chain residual transfer → LSTM correction → learned per-sample gate