{"id":"W3217588902","doi":"","title":"Extraction of nominative entities, an opportunity for the cultural sector ?","year":2015,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.005083868,0.0006340004,0.0005079487,0.003650771,0.002795286,0.006229812,0.001540362,0.001454199,0.008484457],"category_scores_gemma":[0.01471367,0.0005183805,0.0007428235,0.006765103,0.002208746,0.009628572,0.002518841,0.001118382,0.003047696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003339639,"about_ca_system_score_gemma":0.005340705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06170535,"about_ca_topic_score_gemma":0.1024026,"domain_scores_codex":[0.9972101,0.001095327,0.0002397586,0.0003840287,0.000926739,0.000144073],"domain_scores_gemma":[0.991676,0.00311636,0.0005758945,0.001850003,0.002656335,0.0001253512],"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":[0.0002850439,0.0000796834,0.02627676,0.002489458,0.0001706593,0.002073008,0.03713081,0.003177968,0.03059967,0.1709592,0.0292407,0.6975171],"study_design_scores_gemma":[0.00003308935,0.00006394724,0.02458841,0.0009796183,0.0001684127,0.002275312,0.02307842,0.01171901,0.03778172,0.05573737,0.843415,0.0001597709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1936028,0.006341976,0.6357589,0.01319426,0.0005567301,0.0009199224,0.007519449,0.00323851,0.1388675],"genre_scores_gemma":[0.5042598,0.004350584,0.4185734,0.001491318,0.0001313372,0.0004018288,0.01067574,0.000986953,0.05912909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06170535,"threshold_uncertainty_score":0.1226923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1100434543754612,"score_gpt":0.3633509801469527,"score_spread":0.2533075257714915,"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."}}