{"id":"W2949400899","doi":"10.48550/arxiv.1308.6300","title":"Computing Lexical Contrast","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; National Research Council Canada","funders":"","keywords":"Contrast (vision); Meaning (existential); Word (group theory); Computer science; Natural language processing; Artificial intelligence; Lexical item; Mathematics; Linguistics; Psychology; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001962565,0.001004943,0.001495153,0.01102228,0.001947783,0.005429902,0.001224971,0.001557829,0.01262106],"category_scores_gemma":[0.02180549,0.00064053,0.001091512,0.006403075,0.001188804,0.01027864,0.003831242,0.001477007,0.003939301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001334513,"about_ca_system_score_gemma":0.001118273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001687902,"about_ca_topic_score_gemma":0.001836453,"domain_scores_codex":[0.9957029,0.0006299971,0.0005369831,0.001640394,0.001062508,0.0004271891],"domain_scores_gemma":[0.9911948,0.004640833,0.0006331609,0.001177909,0.001863133,0.0004899967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002500416,0.0005583689,0.117828,0.001458237,0.0005718485,0.002124019,0.002343544,0.01023408,0.04334768,0.1343155,0.02971517,0.655003],"study_design_scores_gemma":[0.0003799828,0.0008956317,0.07312678,0.0003878167,0.0007083507,0.004596709,0.005505133,0.2148697,0.03838028,0.5647683,0.09605747,0.0003238155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5711547,0.002872154,0.3219409,0.001535133,0.0009122012,0.0005961641,0.01247901,0.004832864,0.08367696],"genre_scores_gemma":[0.8708991,0.0003619027,0.1139398,0.0002799933,0.0002151376,0.0002946592,0.01049779,0.0003478068,0.003163907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01262106,"threshold_uncertainty_score":0.04222161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08929011440420782,"score_gpt":0.1865830787935839,"score_spread":0.09729296438937611,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}