{"id":"W6997246917","doi":"","title":"Vers une classification sémantique fine des noms dâagent en français","year":2011,"lang":"en","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Noun; Subcategorization; Proper noun; Lexicalization; Part of speech; Semantic property; Lexical item","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016173,0.001135941,0.0007037622,0.002677894,0.004880039,0.007540011,0.0005847638,0.001282249,0.01216282],"category_scores_gemma":[0.002722668,0.0003700236,0.00102648,0.002225439,0.004302935,0.004443361,0.0007731237,0.002248623,0.003698076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01087018,"about_ca_system_score_gemma":0.004720065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2298227,"about_ca_topic_score_gemma":0.1926029,"domain_scores_codex":[0.9981634,0.0006331477,0.00008763119,0.0003962473,0.0005154248,0.0002041489],"domain_scores_gemma":[0.998587,0.0003191862,0.0001402162,0.0001684067,0.000708505,0.00007664517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001132664,0.00003225432,0.004942576,0.0001580757,0.00004178049,0.0003415735,0.01094904,0.0006805243,0.003635743,0.8710895,0.01463726,0.09337843],"study_design_scores_gemma":[0.000022378,0.00004243683,0.01131136,0.0001305483,0.00002793693,0.0005468162,0.005361406,0.001781499,0.002790842,0.03047074,0.9474327,0.0000812934],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2122388,0.009704067,0.1462129,0.008023489,0.001679399,0.0001883662,0.003675257,0.001962034,0.6163157],"genre_scores_gemma":[0.7705377,0.004673935,0.06148731,0.001099151,0.0007037277,0.0001442206,0.004616588,0.0008910042,0.1558464],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2298227,"threshold_uncertainty_score":0.4569699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007011345411973941,"score_gpt":0.1541227136975495,"score_spread":0.1471113682855756,"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."}}