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cc-2021-stat

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Thank you for sharing this sad update. It began early in June. Diego would leave the litter box with a wet back end. I'd have to try to catch him and dry him off. He was also being aggressive towards Cali so after consultation with the... Had Cali at the vet for a health check and shots. Also had the vet look at the tw...	{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nChange the tone of this text to be more relaxed and friendly.\nOutput the full new text with no extra statements or commentations.\nBegin directly with the text itself. Do not add a title, a heading, or a label naming what you have written." ], "use_multi...	Hey there! 😊 So, a little update on our furry family. Diego started having a bit of a wet back end in early June, and I’d have to gently dry him off after he left the litter box. He was also getting a bit feisty with Cali, so we took them both to the vet for a check-up and some shots. Good news – one of Cali’s lumps ...		cyankiwi/granite-4.1-8b-AWQ-INT4	{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}	Hey there! 😊 So, a little update on our furry family. Diego started having a bit of a wet back end in early June, and I’d have to gently dry him off after he left the litter box. He was also getting a bit feisty with Cali, so we took them both to the vet for a check-up and some shots. Good news – one of Cali’s lumps o...	0.730769	0.898167	1,069	0.700098	0.121611	0.154862	0.135461	0.145272	0.571387	-5.3125	29.431168	2.968705	0.052308	0.132308	-3.099721	21.108406	3.051931	0.046154	0.107692	0.017457	12.240019	2.205429	0.052632	0.075188	-3.005851	11.120249	2.555337	0.025063	0.070175	1.392136	0.912657	0.912592	0	0.009655	1	0.409237
CLXVI Sanjaya said, Karna, the son of Vikartana O king, resisted the mighty car-warrior Sahadeva in that battle, who advanced from desire of getting at Drona. Piercing the son of Radha with nine shafts, Sahadeva once more pierced that warrior with nine straight arrows. Karna then pierced Sahadeva in return with a hundr...	{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nModify this text to be as long as possible. At least half of the original text must be repeated verbatim, and at most three quarters of it; the remainder must be new prose.\nOutput the full new text with no extra statements or commentations.\nBegin directly w...	CLXVI Sanjaya said, Karna, the son of Vikartana O king, resisted the mighty car-warrior Sahadeva in that battle, who advanced from desire of getting at Drona. Piercing the son of Radha with nine shafts, Sahadeva once more pierced that warrior with nine straight arrows. Karna then pierced Sahadeva in return with a hundr...		cyankiwi/granite-4.1-8b-AWQ-INT4	{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}	CLXVI Sanjaya said, Karna, the son of Vikartana O king, resisted the mighty car-warrior Sahadeva in that battle, who advanced from desire of getting at Drona. Piercing the son of Radha with nine shafts, Sahadeva once more pierced that warrior with nine straight arrows. Karna then pierced Sahadeva in return with a hundr...	0.272189	0.257143	1,048	0.236559	0.12986	0.042506	0.008047	0.025581	0.23648	-5.875	14.173077	2.463246	0.05227	0.115543	-2.228704	8.895827	2.265456	0.035763	0.07978	0.995518	11.902286	2.321683	0.049145	0.100427	-2.171867	8.743671	2.239163	0.035256	0.077991	1.022023	0.754622	0.789528	0	0.023015	0	0.023015
This processor is one of the most famous one in guitar racks and old studios. It was introduced in 1985 and quickly became successful, it was relatively cheap, and has some very interesting effects algorithms. One of them is the famous ‘Pitch Change C’ algorithm, used by many legendary session guitarists in that era, i...	{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nTake inspiration from the style, language, and content of this text, and write a new piece of text on a topic of your choice. However, you should write the new text as if it was a rough draft being typed out on a phone.\nOutput the full new text with no extra...	I recently decided to dive into the world of vintage synthesizers, specifically a classic Roland Juno-6 that I stumbled upon at a garage sale. This synth, introduced in the early '80s, is renowned for its lush analog sounds and iconic filter sweeps, making it a staple in both electronic and pop music during its heyday....		cyankiwi/granite-4.1-8b-AWQ-INT4	{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}	I recently decided to dive into the world of vintage synthesizers, specifically a classic Roland Juno-6 that I stumbled upon at a garage sale. This synth, introduced in the early '80s, is renowned for its lush analog sounds and iconic filter sweeps, making it a staple in both electronic and pop music during its heyday....	0.867052	0.965714	1,539	0.905363	0.465163	0.214928	0.183499	0.199522	0.66981	3.6875	30.019443	3.32871	0.046099	0.131206	-0.487966	22.984729	3.058342	0.049645	0.113475	-0.5479	6.978194	1.800024	0.041363	0.048662	-1.603477	6.3076	1.893264	0.026764	0.041363	0.577181	0.846486	0.860985	0	0.013802	3	0.999549
This post explains the difference between foodservice operations that are outsourced to contract management companies and those that are 'self-operated' (self-op). You’re definitely going to want to sell to these folks. It’s not an either/or, self-op or contract-managed, situation. You’re going to want to go after both...	{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nChange the tone of this text to be more empathetic and understanding.\nOutput the full new text with no extra statements or commentations.\nBegin directly with the text itself. Do not add a title, a heading, or a label naming what you have written." ], "u...	It's truly exciting to consider the unique opportunities that both outsourced and self-operated foodservice operations present for your business. Understanding the distinctions between these two approaches can guide you in crafting a strategy that resonates deeply with each type of institution, allowing you to build me...		cyankiwi/granite-4.1-8b-AWQ-INT4	{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}	It's truly exciting to consider the unique opportunities that both outsourced and self-operated foodservice operations present for your business. Understanding the distinctions between these two approaches can guide you in crafting a strategy that resonates deeply with each type of institution, allowing you to build me...	0.822857	0.969199	2,820	0.834768	0.102921	0.174497	0.181043	0.177783	0.645246	-5.375	12.161763	2.362016	0.057598	0.072304	-1.86494	9.853105	2.438624	0.033088	0.061275	2.1153	9.992292	2.276658	0.048924	0.068493	-0.292695	9.28057	2.259481	0.039139	0.054795	0.376122	0.877299	0.866542	1	0.301078	3	0.99957
Personalized baskets filled with a selection of foods, personal care products and specialty gifts, hand-crafted in Maine. Biologics, or drugs derived from living cells, often are deemed to be specialty drugs. In the Extensions tab, SDKs that apply to the project from which the Reference Manager dialog box was invoked a...	{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nWrite a detailed descriptor for the style of writing of this text, including its qualitative and quantitative properties.\nDo not output anything besides what you were requested to write, and do not output any extra commentary.", "{{RESP_0}}\nAbove is an ...	The style of writing in this text is characterized by a highly eclectic and disjointed mixture of topics, lacking a coherent structure or thematic continuity. Qualitatively, the text exhibits: 1. Lack of Cohesion: The content jumps abruptly from one subject to another without logical transitions, making it difficu...	It got pretty addicting to watch the biologics market expand, especially with the new Gunsmith system that allowed for more precise modifications. Meanwhile, I was browsing through some personal care products at the local store, wondering if the latest shampoo would really make a difference in my hair's texture. As I s...	cyankiwi/granite-4.1-8b-AWQ-INT4	{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}	It got pretty addicting to watch the biologics market expand, especially with the new Gunsmith system that allowed for more precise modifications. Meanwhile, I was browsing through some personal care products at the local store, wondering if the latest shampoo would really make a difference in my hair's texture. As I s...	0.93698	0.985617	6,211	0.958229	0.756852	0.17024	0.236439	0.204715	0.70685	9.5	43.871325	3.602982	0.055921	0.221053	-2.661363	34.701647	3.389289	0.058553	0.198684	-2.479291	15.273513	2.696525	0.04878	0.126829	-0.196562	16.312344	2.974217	0.039024	0.107317	1.159068	0.902964	0.910813	0	0.015037	3	0.998489
The indie comedy "Me," recently of the Marfa Film Festival, is an always amusing and frequently hilarious commentary on reality shows. The blurred lines between reality and "reality" include writer/producer/director/star Jefery Levy, who is also the creative force behind the recently reviewed sensual drama "The Key," p...	{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nModify this text to be as long as possible. At least half of the original text must be repeated verbatim, and at most three quarters of it; the remainder must be new prose.\nOutput the full new text with no extra statements or commentations.\nBegin directly w...	The indie comedy "Me," recently of the Marfa Film Festival, is an always amusing and frequently hilarious commentary on reality shows. The blurred lines between reality and "reality" include writer/producer/director/star Jefery Levy, who is also the creative force behind the recently reviewed sensual drama "The Key," p...	The indie comedy "Me," recently of the Marfa Film Festival, is an always amusing and frequently hilarious commentary on reality shows. The blurred lines between reality and "reality" include writer/producer/director/star Jefery Levy, who is also the creative force behind the recently reviewed sensual drama "The Key," p...	cyankiwi/granite-4.1-8b-AWQ-INT4	{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}	The indie comedy "Me," recently of the Marfa Film Festival, is an always amusing and frequently hilarious commentary on reality shows. The blurred lines between reality and "reality" include writer/producer/director/star Jefery Levy, who is also the creative force behind the recently reviewed sensual drama "The Key," p...	0.641914	0.668288	5,204	0.657768	0.045973	0.153295	0.021033	0.091955	0.522748	-4.8125	28.527505	3.43263	0.059322	0.188559	0.707636	20.662563	3.054321	0.052966	0.154661	0.237889	9.064062	2.127413	0.053857	0.080058	-1.461245	7.56869	2.261505	0.024745	0.065502	4.375905	0.799293	0.797781	0	0.053711	0	0.108552
Hello all Have only kept for maybe 9/10 games across the last couple of years, but as of next season Ill be keeping full time so am going to be after a new pair of gloves. Been using a pair of Youth Sized B3s but they feel a little too small and havent softened up at all. The problem is that whilst youth sized are a bi...	{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nEdit this text by replacing, removing or inserting twenty to thirty words, without changing anything else.\nOutput the full new text with no extra statements or commentations.\nBegin directly with the text itself. Do not add a title, a heading, or a label nam...	Hello all, I've only kept records for about 9 out of 10 games over the past couple of years, but starting next season, I'll be keeping full-time and need a new pair of gloves. I've been using Youth Sized B3 gloves, but they feel too small and haven't softened up. The issue is that while youth sizes are cramped, regula...	I've only kept records for about 9 out of 10 games over the past couple of years, but starting next season, I'll be keeping full-time and need a new pair of gloves. I've been using Youth Sized B3 gloves, but they feel too small and haven't softened up. The issue is that while youth sizes are cramped, regular men's size...	cyankiwi/granite-4.1-8b-AWQ-INT4	{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}	I've only kept records for about 9 out of 10 games over the past couple of years, but starting next season, I'll be keeping full-time and need a new pair of gloves. I've been using Youth Sized B3 gloves, but they feel too small and haven't softened up. The issue is that while youth sizes are cramped, regular men's size...	0.664948	0.871972	429	0.332998	0.166599	0.116008	0.132431	0.124296	0.521487	-4	32.758523	2.832128	0.084906	0.141509	-4.054773	20.654534	3.042147	0.056604	0.103774	0.089157	22.311907	2.619855	0.096257	0.149733	-3.036921	20.056152	2.987575	0.048128	0.128342	-0.0662	0.907436	0.953395	0	0.008354	1	0.394476
'Stars of Science' regularly showcases inventions across multiple disciplines and sectors that help communities in the Arab world. Multiple contestants throughout the history of the show have developed inventions that focus on preserving their communities’ identity and culture. Two contestants on the current season 13 ...	{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nModify this text to be as long as possible. At least half of the original text must be repeated verbatim, and at most three quarters of it; the remainder must be new prose.\nOutput the full new text with no extra statements or commentations.\nBegin directly w...	'Stars of Science' regularly showcases inventions across multiple disciplines and sectors that help communities in the Arab world. Multiple contestants throughout the history of the show have developed inventions that focus on preserving their communities’ identity and culture. Two contestants on the current season 13...		cyankiwi/granite-4.1-8b-AWQ-INT4	{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}	'Stars of Science' regularly showcases inventions across multiple disciplines and sectors that help communities in the Arab world. Multiple contestants throughout the history of the show have developed inventions that focus on preserving their communities’ identity and culture. Two contestants on the current season 13 ...	0.189252	0.204019	1,010	0.182551	0.007646	0.038291	0.01094	0.024807	0.232088	-3.6875	21.677741	2.968342	0.053748	0.140028	-1.231804	16.629358	2.624869	0.056577	0.117397	-2.310942	16.594063	2.796506	0.045977	0.114943	-0.164582	13.501659	2.499595	0.049425	0.104598	-1.46301	0.861881	0.841646	0	0.096332	0	0.096332
I didn t want to those services, but three jobs statewide. Book offer great places to sit at 0.6 miles 15 years, mandarin. Regulators informally vetoed a planned expansion, announced at a bath seneca model! Additional fees are working closely reviewing cases of this year on the first day, there s website. Still take ad...	{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nWrite a new piece of text that is on a completely different topic, but reuses verbatim as many phrases and sentences from this text as possible.\nOutput the full new text with no extra statements or commentations.\nBegin directly with the text itself. Do not ...	Mgp will remain. Typically, if you can result. Have disease control board. Mail-In-Only voting ended march 16. Shares surged to mgm national harbor resort spa salon. Ricky martin: the city casino resort and human services. Mlb and washington and four shows and the novel coronavirus. Sometimes it will be pushed some cer...		cyankiwi/granite-4.1-8b-AWQ-INT4	{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}	Mgp will remain. Typically, if you can result. Have disease control board. Mail-In-Only voting ended march 16. Shares surged to mgm national harbor resort spa salon. Ricky martin: the city casino resort and human services. Mlb and washington and four shows and the novel coronavirus. Sometimes it will be pushed some cer...	0.686007	0.713137	1,616	0.025714	0.305955	0.036997	0.148212	0.096012	0.536832	-5.5625	141.439919	5.440916	0.035573	0.33004	4.03109	91.023011	4.520366	0.055336	0.284585	0.079273	217.94439	5.624142	0.039735	0.377483	1.069639	98.091889	4.485186	0.072848	0.298013	-0.471177	0.770027	0.739268	0	0.013885	0	0.016192
As a follow up from last week, Monday evening the French President Macron as expected extended our lockdown period to May 11, 2020. However, with this came discussion about how France would slowly start to reopen on that date. A phased in approach starting with getting kids back to school and other industries starting ...	{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nTranslate the given text to Chinese.\nDo not output anything besides what you were requested to write, and do not output any extra commentary.", "{{RESP_0}}\nTranslate this text to English. Output only the English translation with no extra commentary.\nBe...	上周的后续行动中，周一晚上，法国总统马克龙如预期般将封锁期延长至2020年5月11日。然而，随之而来的是关于法国何时逐步恢复运作的讨论。采取分阶段方法，首先让孩子们回到学校，并让其他行业重新开放，引发了乐观情绪。从旅游/旅游角度来看，马克龙尚未解除餐馆的限制，非欧洲人（不包括英国）进入法国进行非必要旅行的国际边境仍然关闭。这意味着什么？目前还没有人能确定。我们知道两家英国航空公司已经建议，非必要旅行的航班将于6月中旬重新启动。欧盟委员会建议在欧洲内部恢复旅行，这对于仍希望今年与我们旅行的英国和欧洲客户来说是好消息。更令人振奋的消息，对于非欧洲客户在秋季有一丝希望——即《困难之路》（Tour de France）将于2020...	On Monday night, French President Macron as expected extended the lockdown until May 11, 2020. However, this has sparked discussions about when France will gradually resume operations. Taking a phased approach, first allowing children to return to school and then reopening other sectors has generated optimistic sentim...	cyankiwi/granite-4.1-8b-AWQ-INT4	{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}	On Monday night, French President Macron as expected extended the lockdown until May 11, 2020. However, this has sparked discussions about when France will gradually resume operations. Taking a phased approach, first allowing children to return to school and then reopening other sectors has generated optimistic sentime...	0.578261	0.790026	607	0.373093	0.040086	0.101076	0.107215	0.104156	0.469829	-8.8125	23.144369	2.90636	0.05102	0.112245	-1.771961	17.595691	2.809396	0.044218	0.091837	-0.454493	22.793286	2.897544	0.049618	0.137405	-1.652082	18.635031	2.765109	0.034351	0.114504	-1.198791	0.886347	0.911059	0	0.010777	1	0.292401
When I committed to NaBloPoMo, I’m not sure that I realized the commitment it would entail. Well, sure, I knew that it meant that I would post daily. It meant that I would be able to refresh my blog with a multitude of recipes that I’ve been dying to share, but for one reason or another, hadn’t blogged yet. The main re...	{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nWrite a detailed descriptor for the manner in which the author of this uses language, and things such as their word choice.\nDo not output anything besides what you were requested to write, and do not output any extra commentary.", "{{RESP_0}}\nAbove is a...	The author employs a warm, inviting, and descriptive language style, characterized by the following elements: 1. Personal Tone: The author uses a first-person narrative, sharing personal reflections and motivations behind committing to NaBloPoMo, which creates an intimate connection with the reader. 2. **Reflecti...	I’m not sure that I realized the commitment it would entail when I decided to take on NaBloPoMo, but here I am, ready to dive in. Life is busy, and sometimes it’s more convenient to write on a Sunday rather than during the hectic weekdays. Today, I want to share something personal—my journey with blogging and a little ...	cyankiwi/granite-4.1-8b-AWQ-INT4	{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}	I’m not sure that I realized the commitment it would entail when I decided to take on NaBloPoMo, but here I am, ready to dive in. Life is busy, and sometimes it’s more convenient to write on a Sunday rather than during the hectic weekdays. Today, I want to share something personal—my journey with blogging and a little ...	0.801115	0.944561	2,258	0.827208	0.243002	0.187242	0.196023	0.191656	0.671135	1.9375	7.319979	1.787634	0.061058	0.050204	-2.901436	4.979841	1.663907	0.027137	0.036635	0.925935	4.510257	1.392892	0.045741	0.029968	-1.884659	4.22261	1.600844	0.025237	0.026814	2.485772	0.718811	0.807867	0	0.040475	3	0.733539
- Bright Star reviews, ratings etc. SPOILERS by Saturn » Sun May 17, 2009 2:56 pm - 312 Replies - 1189380 Views - Last post by Pjerrot Sun Jun 06, 2010 11:38 pm - - Bright Star the movie website! by Saturn » Thu Jun 12, 2008 9:11 pm - 47 Replies - 185336 Views - Last post by Raphael Thu Nov 26, 2009 3:27 pm - - Discu...	{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nEdit this text by replacing, removing or inserting twenty to thirty words, without changing anything else.\nOutput the full new text with no extra statements or commentations.\nBegin directly with the text itself. Do not add a title, a heading, or a label nam...	Bright Star reviews, ratings etc. SPOILERS by Saturn » Sun May 17, 2009 2:56 pm - 312 Replies - 1189380 Views - Last post by Pjerrot Sun Jun 06, 2010 11:38 pm Bright Star the movie website! by Saturn » Thu Jun 12, 2008 9:11 pm - 47 Replies - 185336 Views - Last post by Raphael Thu Nov 26, 2009 ...		cyankiwi/granite-4.1-8b-AWQ-INT4	{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}	Bright Star reviews, ratings etc. SPOILERS by Saturn » Sun May 17, 2009 2:56 pm - 312 Replies - 1189380 Views - Last post by Pjerrot Sun Jun 06, 2010 11:38 pm Bright Star the movie website! by Saturn » Thu Jun 12, 2008 9:11 pm - 47 Replies - 185336 Views - Last post by Raphael Thu Nov 26, 2009 3:27 pm Discussion abou...	0.022857	0.093278	162	0.000119	0.006475	0.020975	0.018915	0.019946	0.22074	-7.8125	4.211267	1.436177	0.023256	0.06538	-0.057009	3.770522	1.358182	0.014919	0.057043	1.15384	4.372434	1.463621	0.025397	0.066667	-0.420288	3.916807	1.396796	0.014512	0.057596	1.143624	0.84123	0.849453	0	0.037114	0	0.03237
CAPSULE: Kaiju Noir. With the Japanese franchise on the King of the Monsters in hiatus, the Godzilla character is being loaned to Warner Brothers so that Gareth Edwards can make an American Godzilla film. This is a script whose drama is better than Toho's usual fare, but audiences may find the new film is dark and drab...	{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nEdit this text by completely rewriting the topic sentence of every paragraph, while preserving the supporting sentences in those paragraphs verbatim.\nOutput the full new text with no extra statements or commentations.\nBegin directly with the text itself. Do...	The new American Godzilla film, directed by Gareth Edwards and starring Bryan Cranston, marks a significant departure from Toho's traditional kaiju fare, offering a darker, more somber narrative that revisits the original film's themes of tragedy and nuclear consequence. In this version, the opening scene shifts focus ...	In this new American Godzilla film, directed by Gareth Edwards and starring Bryan Cranston, a significant departure from Toho's traditional kaiju fare is evident, offering a darker, more somber narrative that revisits the original film's themes of tragedy and nuclear consequence, as the opening scene shifts focus to a ...	cyankiwi/granite-4.1-8b-AWQ-INT4	{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}	In this new American Godzilla film, directed by Gareth Edwards and starring Bryan Cranston, a significant departure from Toho's traditional kaiju fare is evident, offering a darker, more somber narrative that revisits the original film's themes of tragedy and nuclear consequence, as the opening scene shifts focus to a ...	0.872564	0.97343	4,541	0.895899	0.283138	0.161746	0.187128	0.174632	0.651956	-6.0625	20.046873	3.04344	0.05098	0.131765	0.670267	15.815705	2.680367	0.058824	0.111373	-1.282028	15.370043	2.726381	0.044496	0.11007	-0.056481	14.011865	2.664633	0.030445	0.100703	0.2361	0.803702	0.848266	0	0.023796	3	0.988863
Bol Entertainment is an upcoming Pakistani channel that is a subsidiary project of Axact organization, which is the world’s fastest leading Karachi-based IT company. Pakistan Electronic Media Regulatory Authority (PEMRA) has approved radio and satellite television licenses to the BOL Network. The founders of the channe...	{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nChange the tone of this text to be more relaxed and friendly.\nOutput the full new text with no extra statements or commentations.\nBegin directly with the text itself. Do not add a title, a heading, or a label naming what you have written.", "{{RESP_0}}\...	Hey there! So, you might have heard about Bol Entertainment, the super cool new Pakistani channel that's part of the awesome Axact organization. Axact is this super-fast IT company based in Karachi, and they're really shaking things up. The Pakistan Electronic Media Regulatory Authority (PEMRA) just gave the thumbs-up ...	BOL Entertainment, the innovative new Pakistani channel under the umbrella of the dynamic Axact organization, is indeed generating a lot of excitement. Axact, a rapidly growing IT company headquartered in Karachi, is at the forefront of transforming digital landscapes. With the recent approval from the Pakistan Electro...	cyankiwi/granite-4.1-8b-AWQ-INT4	{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}	BOL Entertainment, the innovative new Pakistani channel under the umbrella of the dynamic Axact organization, is indeed generating a lot of excitement. Axact, a rapidly growing IT company headquartered in Karachi, is at the forefront of transforming digital landscapes. With the recent approval from the Pakistan Electro...	0.848889	0.946328	1,656	0.869361	0.191256	0.240841	0.125479	0.187233	0.719341	2.375	28.105714	2.704589	0.063158	0.168421	-2.611264	22.5573	2.542473	0.115789	0.136842	-2.523425	7.200448	1.964177	0.032698	0.046322	-0.103899	6.38383	2.012511	0.021798	0.049046	1.661662	1.019556	0.807326	1	0.25899	3	0.999501
SAT Practice: Essay - Amazon Web Services. Scores for the samples provided below were assigned on a 1-4 scale according to the redesigned SAT Essay Scoring Rubric. It is important to note that although these are representative samples of student ability at each score point, the set itself does not exhaustively illustra...	{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nChange the tone of this text to be more suited for a business audience.\nOutput the full new text with no extra statements or commentations.\nBegin directly with the text itself. Do not add a title, a heading, or a label naming what you have written." ], ...	SAT Practice: Essay - Amazon Web Services. Scores for the samples provided below were assigned on a 1-4 scale according to the redesigned SAT Essay Scoring Rubric. These samples represent student ability at each score point but do not exhaustively cover the range of skills in Reading, Analysis, and Writing associated ...		cyankiwi/granite-4.1-8b-AWQ-INT4	{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}	SAT Practice: Essay - Amazon Web Services. Scores for the samples provided below were assigned on a 1-4 scale according to the redesigned SAT Essay Scoring Rubric. These samples represent student ability at each score point but do not exhaustively cover the range of skills in Reading, Analysis, and Writing associated w...	0.717557	0.864211	2,662	0.312343	0.042347	0.098328	0.139089	0.11918	0.528709	-7.4375	15.777492	2.535348	0.065817	0.096603	-3.07108	8.389224	2.110075	0.044586	0.069002	-0.254875	19.837715	2.816855	0.05	0.114	-1.663618	19.182255	2.544328	0.068	0.12	-4.191767	0.608575	0.904538	0	0.008327	1	0.283292

End of preview. Expand in Data Studio

Auto-Generated FastDetector Dataset

Dataset: G-reen/cc-2021-stat
Globals Config: config/globals_cc2021.toml
Analysis Config: config/analysis.toml
Rows Loaded: 23,214
Filter Conditions: cosdist >= 0.03 OR softngram >= 0.06
Rows Analyzed (after filtering): 20,895
Evaluation / Validation Rows: 18,805 / 2,090 (validation_size = 0.1)
Base Columns: original (Human), final_response (AI)
Distance Metrics: jaccard_1, jaccard_2, levenshtein, softngram, cosdist, bertscore, bertscore_precision, bertscore_recall, moverscore, reranker
Distance Metrics Skipped (not in this dataset): jaccard_3
Prompt Subsets: 4 (direct_reference, indirect_reference, revise, rewrite)
Generator Configs: 7 (Meta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 0.6), Meta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 1.25), Ministral-3-8B-Instruct-2512-AWQ-4bit (Temp: 0.6), Qwen3-8B-AWQ (Temp: 0.7), gemma-4-E4B-it (Temp: 0.7), granite-4.1-8b-AWQ-INT4 (Temp: 0.6), granite-4.1-8b-AWQ-INT4 (Temp: 1.25))
Classifiers:
- EditLens Roberta-Large Score - columns *_editlens_score_roberta_large, direction higher_is_ai (swept for fpr_0_5pct on the validation split)
- EditLens Roberta-Large Bucket - columns *_editlens_bucket_roberta_large, direction higher_is_ai (swept for f1 on the validation split)
- Perplexity (Llama-3.2-3B-Instruct) - columns *_perplexity_llama_instruct, direction lower_is_ai (swept for fpr_0_5pct on the validation split)
- Perplexity (Llama-3.2-3B) - columns *_perplexity_llama_base, direction lower_is_ai (swept for fpr_0_5pct on the validation split)
- Entropy (Llama-3.2-3B-Instruct) - columns *_entropy_llama_instruct, direction lower_is_ai (swept for fpr_0_5pct on the validation split)
- Entropy (Llama-3.2-3B) - columns *_entropy_llama_base, direction lower_is_ai (swept for fpr_0_5pct on the validation split)
- Top-p Outliers (Llama-3.2-3B-Instruct) - columns *_topp_outlier_llama_instruct, direction lower_is_ai (swept for fpr_0_5pct on the validation split)
- Top-p Outliers (Llama-3.2-3B) - columns *_topp_outlier_llama_base, direction lower_is_ai (swept for fpr_0_5pct on the validation split)
- Top-k Outliers (Llama-3.2-3B-Instruct) - columns *_topk_outlier_llama_instruct, direction lower_is_ai (swept for fpr_0_5pct on the validation split)
- Top-k Outliers (Llama-3.2-3B) - columns *_topk_outlier_llama_base, direction lower_is_ai (swept for fpr_0_5pct on the validation split)
- FastDetectGPT (Llama-3.2-3B-Instruct) - columns *_fastdetectgpt_llama_instruct, direction higher_is_ai (swept for fpr_0_5pct on the validation split)
- FastDetectGPT (Llama-3.2-3B) - columns *_fastdetectgpt_llama_base, direction lower_is_ai (swept for fpr_0_5pct on the validation split)
- Binoculars - columns *_binoculars, direction higher_is_ai (swept for fpr_0_5pct on the validation split)

This readme computes the detection report for G-reen/cc-2021-stat: 20,895 human/AI text pairs from 7 generator configuration(s) and 4 prompt type(s), summarised univariately, correlated against each other, and used to score 13 classifier(s) as AI-text detectors. Over the whole evaluation split EditLens Roberta-Large Score separates the two classes best (AUROC 0.9100, catching 36.95% of AI rows at 0.30% false positives), while Perplexity (Llama-3.2-3B) is weakest (AUROC 0.4837). 10 pairwise distance metric(s) (jaccard_1, jaccard_2, levenshtein, softngram, cosdist, bertscore, bertscore_precision, bertscore_recall, moverscore, reranker) measure how far each AI response moved from its human original; they are profiled here and correlated against every classifier score. Every classifier is then broken down over 4 prompt subset(s). For EditLens Roberta-Large Score, the easiest subset is revise (AUROC 0.9787) and the hardest rewrite (AUROC 0.8440). The final section reports EditLens Roberta-Large Score across the 7 generator configuration(s) that wrote the AI side of the corpus.

Univariate Analysis
Correlation Heatmap
Histogram, Distances
- Distance Histograms per Prompt Subset
Histogram, Classification
Classifiers Comparison Table
Classifier Thresholds
Classifier Report: EditLens Roberta-Large Score
- EditLens Roberta-Large Score: By Prompt Subset
- EditLens Roberta-Large Score: Score Histograms per Prompt Subset
Classifier Report: EditLens Roberta-Large Bucket
- EditLens Roberta-Large Bucket: By Prompt Subset
- EditLens Roberta-Large Bucket: Score Histograms per Prompt Subset
Classifier Report: Perplexity (Llama-3.2-3B-Instruct)
- Perplexity (Llama-3.2-3B-Instruct): By Prompt Subset
- Perplexity (Llama-3.2-3B-Instruct): Score Histograms per Prompt Subset
Classifier Report: Perplexity (Llama-3.2-3B)
- Perplexity (Llama-3.2-3B): By Prompt Subset
- Perplexity (Llama-3.2-3B): Score Histograms per Prompt Subset
Classifier Report: Entropy (Llama-3.2-3B-Instruct)
- Entropy (Llama-3.2-3B-Instruct): By Prompt Subset
- Entropy (Llama-3.2-3B-Instruct): Score Histograms per Prompt Subset
Classifier Report: Entropy (Llama-3.2-3B)
- Entropy (Llama-3.2-3B): By Prompt Subset
- Entropy (Llama-3.2-3B): Score Histograms per Prompt Subset
Classifier Report: Top-p Outliers (Llama-3.2-3B-Instruct)
- Top-p Outliers (Llama-3.2-3B-Instruct): By Prompt Subset
- Top-p Outliers (Llama-3.2-3B-Instruct): Score Histograms per Prompt Subset
Classifier Report: Top-p Outliers (Llama-3.2-3B)
- Top-p Outliers (Llama-3.2-3B): By Prompt Subset
- Top-p Outliers (Llama-3.2-3B): Score Histograms per Prompt Subset
Classifier Report: Top-k Outliers (Llama-3.2-3B-Instruct)
- Top-k Outliers (Llama-3.2-3B-Instruct): By Prompt Subset
- Top-k Outliers (Llama-3.2-3B-Instruct): Score Histograms per Prompt Subset
Classifier Report: Top-k Outliers (Llama-3.2-3B)
- Top-k Outliers (Llama-3.2-3B): By Prompt Subset
- Top-k Outliers (Llama-3.2-3B): Score Histograms per Prompt Subset
Classifier Report: FastDetectGPT (Llama-3.2-3B-Instruct)
- FastDetectGPT (Llama-3.2-3B-Instruct): By Prompt Subset
- FastDetectGPT (Llama-3.2-3B-Instruct): Score Histograms per Prompt Subset
Classifier Report: FastDetectGPT (Llama-3.2-3B)
- FastDetectGPT (Llama-3.2-3B): By Prompt Subset
- FastDetectGPT (Llama-3.2-3B): Score Histograms per Prompt Subset
Classifier Report: Binoculars
- Binoculars: By Prompt Subset
- Binoculars: Score Histograms per Prompt Subset
Manually Specified Full Report
- Score Histograms per Generator Config
- Distance Histograms per Generator Config

Univariate Analysis

Every statistic the report does arithmetic on, over the 18,805-row evaluation split. Invalid/Error counts rows whose value is missing or non-finite; those rows are excluded from the other columns.

Statistic	N	Mean	Median	Std Dev	Min	Max
jaccard_1	18,805	0.7030	0.7715	0.2112	0.0000	1.0000
jaccard_2	18,805	0.8274	0.9206	0.2134	0.0000	1.0000
levenshtein	18,805	2534.6936	1563.0000	3354.9315	1.0000	87016.0000
softngram	18,805	0.6562	0.7398	0.3054	0.0000	1.0000
cosdist	18,805	0.2269	0.1622	0.1977	-0.0038	1.0211
bertscore	18,805	0.1599	0.1630	0.0719	0.0011	0.4992
bertscore_precision	18,805	0.1585	0.1611	0.0736	0.0011	0.4512
bertscore_recall	18,805	0.1600	0.1614	0.0767	0.0006	0.5874
moverscore	18,805	0.5951	0.6219	0.1636	0.0616	1.1348
reranker	18,805	-2.3292	-4.0000	5.6764	-11.0000	18.7500
EditLens Roberta-Large Score (Human)	18,805	0.0566	0.0210	0.0911	0.0063	0.9995
EditLens Roberta-Large Score (AI)	18,805	0.5179	0.4536	0.3547	0.0065	0.9996
EditLens Roberta-Large Bucket (Human)	18,805	0.0606	0.0000	0.2861	0.0000	3.0000
EditLens Roberta-Large Bucket (AI)	18,805	1.4916	1.0000	1.2379	0.0000	3.0000
Perplexity (Llama-3.2-3B-Instruct) (Human)	18,805	19.1317	14.5347	32.1309	1.0554	2279.9307
Perplexity (Llama-3.2-3B-Instruct) (AI)	18,805	90.7528	12.9654	464.2314	1.0655	10003.4067
Perplexity (Llama-3.2-3B) (Human)	18,805	13.8853	11.1024	20.3824	1.0282	1479.0899
Perplexity (Llama-3.2-3B) (AI)	18,805	98.7711	11.2350	552.4906	1.0638	13124.9201
Entropy (Llama-3.2-3B-Instruct) (Human)	18,805	2.5292	2.4915	0.6326	0.0629	7.8258
Entropy (Llama-3.2-3B-Instruct) (AI)	18,805	2.6173	2.3913	1.2152	0.1077	9.3056
Entropy (Llama-3.2-3B) (Human)	18,805	2.3851	2.4026	0.5737	0.0411	6.3306
Entropy (Llama-3.2-3B) (AI)	18,805	2.5813	2.4044	1.0041	0.0909	8.3789
Top-p Outliers (Llama-3.2-3B-Instruct) (Human)	18,805	0.0555	0.0536	0.0164	0.0000	0.2045
Top-p Outliers (Llama-3.2-3B-Instruct) (AI)	18,805	0.0520	0.0505	0.0175	0.0000	0.2308
Top-p Outliers (Llama-3.2-3B) (Human)	18,805	0.0416	0.0413	0.0127	0.0000	0.1774
Top-p Outliers (Llama-3.2-3B) (AI)	18,805	0.0426	0.0400	0.0202	0.0000	0.2222
Top-k Outliers (Llama-3.2-3B-Instruct) (Human)	18,805	0.1063	0.0975	0.0526	0.0000	0.6452
Top-k Outliers (Llama-3.2-3B-Instruct) (AI)	18,805	0.1188	0.0851	0.1299	0.0000	0.8796
Top-k Outliers (Llama-3.2-3B) (Human)	18,805	0.0871	0.0792	0.0478	0.0000	0.5529
Top-k Outliers (Llama-3.2-3B) (AI)	18,805	0.1106	0.0758	0.1297	0.0000	0.8802
FastDetectGPT (Llama-3.2-3B-Instruct) (Human)	18,805	-1.6726	-1.7422	1.9346	-17.4604	25.2161
FastDetectGPT (Llama-3.2-3B-Instruct) (AI)	18,805	-1.1485	-1.3163	1.9614	-10.2159	19.4413
FastDetectGPT (Llama-3.2-3B) (Human)	18,805	-0.1765	-0.1421	1.1085	-7.3730	5.5766
FastDetectGPT (Llama-3.2-3B) (AI)	18,805	-0.7953	-0.2777	4.0068	-38.9644	16.2603
Binoculars (Human)	18,805	0.8279	0.8520	0.1319	0.0075	1.2099
Binoculars (AI)	18,805	0.8793	0.8829	0.0836	0.0511	1.1840

Correlation Heatmap

Pearson correlation between every statistic of interest, computed over the rows where both statistics are present.

Histogram, Distances

Distribution of each pairwise distance between a human original and its AI rewrite, over the whole evaluation split.

Distance Histograms per Prompt Subset

The same distances, split by the 4 prompt subset(s).

Histogram, Classification

Human and AI score distributions for each classifier, overlaid, over the whole evaluation split.

Classifiers Comparison Table

AUROC is threshold-free; TPR, FPR, accuracy and F1 are measured at each classifier's own pinned threshold (shown in the first column). Rows a classifier produced no usable score for are excluded from its metrics and counted in the univariate table's Invalid/Error column.

Classifier	Threshold	AUROC	TPR @ Threshold	FPR @ Threshold	Accuracy	F1
✔️ EditLens Roberta-Large Score	0.6585	0.9100	0.3695	0.0030	0.6832	0.5384
EditLens Roberta-Large Bucket	0.0000	0.8485	0.7321	0.0523	0.8399	0.8206
Perplexity (Llama-3.2-3B-Instruct)	1.5574	0.5519	0.0009	0.0006	0.5002	0.0018
❗ Perplexity (Llama-3.2-3B)	1.1373	0.4837	0.0002	0.0020	0.4991	0.0004
Entropy (Llama-3.2-3B-Instruct)	0.8337	0.5426	0.0051	0.0054	0.4999	0.0101
Entropy (Llama-3.2-3B)	0.2886	0.4852	0.0004	0.0051	0.4976	0.0007
Top-p Outliers (Llama-3.2-3B-Instruct)	0.0202	0.5644	0.0223	0.0076	0.5074	0.0433
Top-p Outliers (Llama-3.2-3B)	0.0043	0.5184	0.0043	0.0068	0.4988	0.0084
Top-k Outliers (Llama-3.2-3B-Instruct)	0.0084	0.5697	0.0129	0.0031	0.5049	0.0254
Top-k Outliers (Llama-3.2-3B)	0.0000	0.5179	0.0005	0.0023	0.4991	0.0011
FastDetectGPT (Llama-3.2-3B-Instruct)	9.0102	0.5797	0.0009	0.0024	0.4992	0.0017
FastDetectGPT (Llama-3.2-3B)	-3.6961	0.5178	0.0955	0.0031	0.5462	0.1738
Binoculars	1.0238	0.6438	0.0257	0.0051	0.5103	0.0498

✔️ marks the best AUROC, ❗ the worst.

Classifier Thresholds

Accuracy against threshold on the 2,090-row validation split, with each candidate threshold type marked. The type named in the run configuration is the one pinned for the tables above.

EditLens Roberta-Large Score (swept for fpr_0_5pct on the validation split)

EditLens Roberta-Large Bucket (swept for f1 on the validation split)

Perplexity (Llama-3.2-3B-Instruct) (swept for fpr_0_5pct on the validation split)

Perplexity (Llama-3.2-3B) (swept for fpr_0_5pct on the validation split)

Entropy (Llama-3.2-3B-Instruct) (swept for fpr_0_5pct on the validation split)

Entropy (Llama-3.2-3B) (swept for fpr_0_5pct on the validation split)

Top-p Outliers (Llama-3.2-3B-Instruct) (swept for fpr_0_5pct on the validation split)

Top-p Outliers (Llama-3.2-3B) (swept for fpr_0_5pct on the validation split)

Top-k Outliers (Llama-3.2-3B-Instruct) (swept for fpr_0_5pct on the validation split)

Top-k Outliers (Llama-3.2-3B) (swept for fpr_0_5pct on the validation split)

FastDetectGPT (Llama-3.2-3B-Instruct) (swept for fpr_0_5pct on the validation split)

FastDetectGPT (Llama-3.2-3B) (swept for fpr_0_5pct on the validation split)

Binoculars (swept for fpr_0_5pct on the validation split)

Classifier Report: EditLens Roberta-Large Score

Threshold swept for fpr_0_5pct on the validation split = 0.6585; scores read from *_editlens_score_roberta_large (higher_is_ai).

EditLens Roberta-Large Score: By Prompt Subset

Prompt Subset	N	AUROC	TPR	FPR	Accuracy	F1
Overall	37,610	0.9100	0.3695	0.0030	0.6832	0.5384
direct_reference	9,712	0.8605	0.4154	0.0027	0.7063	0.5858
indirect_reference	9,960	0.9386	0.4185	0.0030	0.7077	0.5888
✔️ revise	10,278	0.9787	0.4343	0.0023	0.7160	0.6046
❗ rewrite	7,660	0.8440	0.1606	0.0044	0.5781	0.2757

EditLens Roberta-Large Score: Score Histograms per Prompt Subset

Classifier Report: EditLens Roberta-Large Bucket

Threshold swept for f1 on the validation split = 0.0000; scores read from *_editlens_bucket_roberta_large (higher_is_ai).

EditLens Roberta-Large Bucket: By Prompt Subset

Prompt Subset	N	AUROC	TPR	FPR	Accuracy	F1
Overall	37,610	0.8485	0.7321	0.0523	0.8399	0.8206
direct_reference	9,712	0.8084	0.6501	0.0535	0.7983	0.7632
indirect_reference	9,960	0.8580	0.7490	0.0532	0.8479	0.8312
✔️ revise	10,278	0.9398	0.9091	0.0490	0.9300	0.9286
❗ rewrite	7,660	0.7643	0.5768	0.0540	0.7614	0.7073

EditLens Roberta-Large Bucket: Score Histograms per Prompt Subset

Classifier Report: Perplexity (Llama-3.2-3B-Instruct)

Threshold swept for fpr_0_5pct on the validation split = 1.5574; scores read from *_perplexity_llama_instruct (lower_is_ai).

Perplexity (Llama-3.2-3B-Instruct): By Prompt Subset

Prompt Subset	N	AUROC	TPR	FPR	Accuracy	F1
Overall	37,610	0.5519	0.0009	0.0006	0.5002	0.0018
direct_reference	9,712	0.5812	0.0002	0.0006	0.4998	0.0004
indirect_reference	9,960	0.5564	0.0026	0.0002	0.5012	0.0052
✔️ revise	10,278	0.5887	0.0002	0.0012	0.4995	0.0004
❗ rewrite	7,660	0.4592	0.0005	0.0003	0.5001	0.0010

Perplexity (Llama-3.2-3B-Instruct): Score Histograms per Prompt Subset

Classifier Report: Perplexity (Llama-3.2-3B)

Threshold swept for fpr_0_5pct on the validation split = 1.1373; scores read from *_perplexity_llama_base (lower_is_ai).

Perplexity (Llama-3.2-3B): By Prompt Subset

Prompt Subset	N	AUROC	TPR	FPR	Accuracy	F1
Overall	37,610	0.4837	0.0002	0.0020	0.4991	0.0004
direct_reference	9,712	0.5066	0.0000	0.0016	0.4992	0.0000
indirect_reference	9,960	0.4819	0.0004	0.0010	0.4997	0.0008
✔️ revise	10,278	0.5140	0.0000	0.0031	0.4984	0.0000
❗ rewrite	7,660	0.4156	0.0005	0.0021	0.4992	0.0010

Perplexity (Llama-3.2-3B): Score Histograms per Prompt Subset

Classifier Report: Entropy (Llama-3.2-3B-Instruct)

Threshold swept for fpr_0_5pct on the validation split = 0.8337; scores read from *_entropy_llama_instruct (lower_is_ai).

Entropy (Llama-3.2-3B-Instruct): By Prompt Subset

Prompt Subset	N	AUROC	TPR	FPR	Accuracy	F1
Overall	37,610	0.5426	0.0051	0.0054	0.4999	0.0101
direct_reference	9,712	0.5695	0.0066	0.0058	0.5004	0.0130
indirect_reference	9,960	0.5395	0.0104	0.0030	0.5037	0.0206
✔️ revise	10,278	0.5808	0.0010	0.0062	0.4974	0.0019
❗ rewrite	7,660	0.4606	0.0018	0.0068	0.4975	0.0036

Entropy (Llama-3.2-3B-Instruct): Score Histograms per Prompt Subset

Classifier Report: Entropy (Llama-3.2-3B)

Threshold swept for fpr_0_5pct on the validation split = 0.2886; scores read from *_entropy_llama_base (lower_is_ai).

Entropy (Llama-3.2-3B): By Prompt Subset

Prompt Subset	N	AUROC	TPR	FPR	Accuracy	F1
Overall	37,610	0.4852	0.0004	0.0051	0.4976	0.0007
direct_reference	9,712	0.4735	0.0000	0.0056	0.4972	0.0000
indirect_reference	9,960	0.4752	0.0008	0.0034	0.4987	0.0016
✔️ revise	10,278	0.5342	0.0000	0.0062	0.4969	0.0000
❗ rewrite	7,660	0.4461	0.0008	0.0052	0.4978	0.0016

Entropy (Llama-3.2-3B): Score Histograms per Prompt Subset

Classifier Report: Top-p Outliers (Llama-3.2-3B-Instruct)

Threshold swept for fpr_0_5pct on the validation split = 0.0202; scores read from *_topp_outlier_llama_instruct (lower_is_ai).

Top-p Outliers (Llama-3.2-3B-Instruct): By Prompt Subset

Prompt Subset	N	AUROC	TPR	FPR	Accuracy	F1
Overall	37,610	0.5644	0.0223	0.0076	0.5074	0.0433
direct_reference	9,712	0.5882	0.0362	0.0070	0.5146	0.0695
✔️ indirect_reference	9,960	0.5963	0.0412	0.0066	0.5173	0.0786
❗ revise	10,278	0.5331	0.0039	0.0074	0.4982	0.0077
rewrite	7,660	0.5350	0.0047	0.0097	0.4975	0.0093

Top-p Outliers (Llama-3.2-3B-Instruct): Score Histograms per Prompt Subset

Classifier Report: Top-p Outliers (Llama-3.2-3B)

Threshold swept for fpr_0_5pct on the validation split = 0.0043; scores read from *_topp_outlier_llama_base (lower_is_ai).

Top-p Outliers (Llama-3.2-3B): By Prompt Subset

Prompt Subset	N	AUROC	TPR	FPR	Accuracy	F1
Overall	37,610	0.5184	0.0043	0.0068	0.4988	0.0084
✔️ direct_reference	9,712	0.6174	0.0060	0.0070	0.4995	0.0118
indirect_reference	9,960	0.5216	0.0090	0.0058	0.5016	0.0178
revise	10,278	0.4931	0.0006	0.0074	0.4966	0.0012
❗ rewrite	7,660	0.4245	0.0008	0.0068	0.4970	0.0016

Top-p Outliers (Llama-3.2-3B): Score Histograms per Prompt Subset

Classifier Report: Top-k Outliers (Llama-3.2-3B-Instruct)

Threshold swept for fpr_0_5pct on the validation split = 0.0084; scores read from *_topk_outlier_llama_instruct (lower_is_ai).

Top-k Outliers (Llama-3.2-3B-Instruct): By Prompt Subset

Prompt Subset	N	AUROC	TPR	FPR	Accuracy	F1
Overall	37,610	0.5697	0.0129	0.0031	0.5049	0.0254
✔️ direct_reference	9,712	0.6220	0.0237	0.0039	0.5099	0.0461
indirect_reference	9,960	0.5634	0.0227	0.0012	0.5107	0.0443
revise	10,278	0.6027	0.0025	0.0035	0.4995	0.0050
❗ rewrite	7,660	0.4668	0.0005	0.0039	0.4983	0.0010

Top-k Outliers (Llama-3.2-3B-Instruct): Score Histograms per Prompt Subset

Classifier Report: Top-k Outliers (Llama-3.2-3B)

Threshold swept for fpr_0_5pct on the validation split = 0.0000; scores read from *_topk_outlier_llama_base (lower_is_ai).

Top-k Outliers (Llama-3.2-3B): By Prompt Subset

Prompt Subset	N	AUROC	TPR	FPR	Accuracy	F1
Overall	37,610	0.5179	0.0005	0.0023	0.4991	0.0011
✔️ direct_reference	9,712	0.5654	0.0008	0.0033	0.4988	0.0016
indirect_reference	9,960	0.5077	0.0010	0.0008	0.5001	0.0020
revise	10,278	0.5465	0.0002	0.0019	0.4991	0.0004
❗ rewrite	7,660	0.4320	0.0000	0.0034	0.4983	0.0000

Top-k Outliers (Llama-3.2-3B): Score Histograms per Prompt Subset

Classifier Report: FastDetectGPT (Llama-3.2-3B-Instruct)

Threshold swept for fpr_0_5pct on the validation split = 9.0102; scores read from *_fastdetectgpt_llama_instruct (higher_is_ai).

FastDetectGPT (Llama-3.2-3B-Instruct): By Prompt Subset

Prompt Subset	N	AUROC	TPR	FPR	Accuracy	F1
Overall	37,610	0.5797	0.0009	0.0024	0.4992	0.0017
direct_reference	9,712	0.5969	0.0014	0.0033	0.4991	0.0029
✔️ indirect_reference	9,960	0.6284	0.0006	0.0026	0.4990	0.0012
revise	10,278	0.5443	0.0006	0.0023	0.4991	0.0012
❗ rewrite	7,660	0.5424	0.0008	0.0013	0.4997	0.0016

FastDetectGPT (Llama-3.2-3B-Instruct): Score Histograms per Prompt Subset

Classifier Report: FastDetectGPT (Llama-3.2-3B)

Threshold swept for fpr_0_5pct on the validation split = -3.6961; scores read from *_fastdetectgpt_llama_base (lower_is_ai).

FastDetectGPT (Llama-3.2-3B): By Prompt Subset

Prompt Subset	N	AUROC	TPR	FPR	Accuracy	F1
Overall	37,610	0.5178	0.0955	0.0031	0.5462	0.1738
❗ direct_reference	9,712	0.4234	0.0673	0.0037	0.5318	0.1257
indirect_reference	9,960	0.4878	0.1084	0.0030	0.5527	0.1951
revise	10,278	0.5588	0.0880	0.0027	0.5426	0.1613
✔️ rewrite	7,660	0.6216	0.1243	0.0031	0.5606	0.2205

FastDetectGPT (Llama-3.2-3B): Score Histograms per Prompt Subset

Classifier Report: Binoculars

Threshold swept for fpr_0_5pct on the validation split = 1.0238; scores read from *_binoculars (higher_is_ai).

Binoculars: By Prompt Subset

Prompt Subset	N	AUROC	TPR	FPR	Accuracy	F1
Overall	37,610	0.6438	0.0257	0.0051	0.5103	0.0498
❗ direct_reference	9,712	0.6056	0.0247	0.0056	0.5096	0.0480
indirect_reference	9,960	0.6553	0.0357	0.0062	0.5148	0.0686
✔️ revise	10,278	0.6715	0.0163	0.0049	0.5057	0.0320
rewrite	7,660	0.6398	0.0264	0.0034	0.5115	0.0512

Binoculars: Score Histograms per Prompt Subset

Manually Specified Full Report

This section is hardcoded: it reports the first configured classifier (EditLens Roberta-Large Score) over the generator model/sampling configurations that produced the AI side of each pair.

Generator Config	N	AUROC	TPR	FPR	Accuracy	F1
Overall	37,610	0.9100	0.3695	0.0030	0.6832	0.5384
❗ Meta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 0.6)	5,324	0.8428	0.2667	0.0038	0.6315	0.4199
Meta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 1.25)	5,842	0.8629	0.1513	0.0045	0.5734	0.2618
✔️ Ministral-3-8B-Instruct-2512-AWQ-4bit (Temp: 0.6)	5,860	0.9910	0.6020	0.0024	0.7998	0.7505
Qwen3-8B-AWQ (Temp: 0.7)	4,570	0.8857	0.3707	0.0044	0.6832	0.5391
gemma-4-E4B-it (Temp: 0.7)	5,038	0.9031	0.2664	0.0024	0.6320	0.4199
granite-4.1-8b-AWQ-INT4 (Temp: 0.6)	5,376	0.9293	0.4498	0.0011	0.7243	0.6200
granite-4.1-8b-AWQ-INT4 (Temp: 1.25)	5,600	0.9459	0.4661	0.0029	0.7316	0.6346