{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002009445,0.0002755664,0.000330584,0.0001479361,0.0001429666,0.0002116251,0.002182575,0.0002908681,0.00004200164],"category_scores_gemma":[0.00002696488,0.0003189614,0.000180032,0.0002331938,0.00007871015,0.0002974556,0.003096654,0.0007065609,0.0002719224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001501715,"about_ca_system_score_gemma":0.0001534166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002639369,"about_ca_topic_score_gemma":0.00001090516,"domain_scores_codex":[0.9980102,0.0001113337,0.0002180303,0.001144145,0.00009602121,0.00042024],"domain_scores_gemma":[0.9981103,0.0001088233,0.0001854915,0.001262605,0.0001332753,0.0001994952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003554839,0.00005125591,0.001372537,0.00004541721,0.00005533947,0.0001860609,0.0002238391,0.294167,0.00002691589,0.6987277,0.0003949116,0.004745544],"study_design_scores_gemma":[0.0002540306,0.0000136172,0.0005897474,0.00007411641,0.00001676487,0.000004875835,0.00002358365,0.9488301,0.00003461889,0.0494898,0.0003371781,0.0003315672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2389278,0.00003975346,0.756033,0.0001385287,0.0006866786,0.0001779428,0.000001953434,0.0003135522,0.003680719],"genre_scores_gemma":[0.9876257,0.00002293599,0.01106067,0.0001938043,0.0001707613,2.99303e-7,0.000004165083,0.00001327748,0.0009084606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7486978,"threshold_uncertainty_score":0.9999263,"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."}}