{"id":"W1645058015","doi":"","title":"Seeing Red: Terminal Description and Explanation in Linguistics","year":2013,"lang":"en","type":"article","venue":"The Journal of Macrodynamic Analysis (Memorial University of Newfoundland)","topic":"Syntax, Semantics, Linguistic Variation","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Adjective; Linguistics; Noun; Meaning (existential); Grammar; Semantics (computer science); Term (time); Lexical item; Value (mathematics); Computer science; Proper noun; Interpretation (philosophy); Mathematics; Natural language processing; Philosophy; Epistemology","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":[],"consensus_categories":[],"category_scores_codex":[0.000684667,0.00009964003,0.0003191945,0.0004754177,0.0001925539,0.00007336811,0.0002108632,0.00004675096,0.0003022872],"category_scores_gemma":[0.0003790863,0.00008371484,0.0001119511,0.0001447543,0.0001579478,0.0001789781,0.00004201857,0.0001659552,0.000004599326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001471649,"about_ca_system_score_gemma":0.00004245445,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01908881,"about_ca_topic_score_gemma":0.03882868,"domain_scores_codex":[0.9990001,0.0001508889,0.0003728196,0.00008986595,0.0002647507,0.0001215654],"domain_scores_gemma":[0.9983846,0.0002038013,0.0006550043,0.0001228654,0.0005910738,0.00004266502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003762077,0.001186698,0.279424,0.000401139,0.008903223,0.000394875,0.4278968,0.03444907,0.003897499,0.2244432,0.001840758,0.01340069],"study_design_scores_gemma":[0.007263741,0.0009149389,0.343581,0.0003834845,0.01328047,0.00009591416,0.1207314,0.3666658,0.00001565413,0.1411791,0.004946369,0.0009421422],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906905,0.00002936348,0.005000664,0.001215286,0.001023325,0.00009375169,0.000006770115,0.000006948688,0.001933416],"genre_scores_gemma":[0.9980668,0.0000869921,0.0004551698,0.0000115656,0.0009778203,3.993621e-8,0.00001028219,0.000005475788,0.0003858638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3322168,"threshold_uncertainty_score":0.9874431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01508010081497349,"score_gpt":0.2055427092133711,"score_spread":0.1904626083983976,"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."}}