{"id":"W2607147011","doi":"10.71781/10686","title":"Extraction d'information à partir de transcription de conversations téléphoniques spécialisées","year":2004,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Information extraction; Extraction (chemistry); Transcription (linguistics); Computer science; Computational biology; Humanities; Biology; Chemistry; Artificial intelligence; Chromatography; Philosophy; Linguistics","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":[0.0008287237,0.001777526,0.001287839,0.004482644,0.001121711,0.002372247,0.000825558,0.001650254,0.016006],"category_scores_gemma":[0.004756854,0.0006370367,0.001195202,0.003465546,0.0005751937,0.001808442,0.001175503,0.00132539,0.01289086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000778754,"about_ca_system_score_gemma":0.001712807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01084477,"about_ca_topic_score_gemma":0.01458019,"domain_scores_codex":[0.9983543,0.0004090333,0.0001393403,0.0004829811,0.0003833376,0.0002309694],"domain_scores_gemma":[0.9964204,0.001484599,0.0001900092,0.000500951,0.001258615,0.0001454611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001132512,0.0001908413,0.003859441,0.001851352,0.0001865528,0.001876836,0.002500433,0.001707926,0.1990387,0.00331675,0.05420781,0.7301307],"study_design_scores_gemma":[0.0002907746,0.000617863,0.06631207,0.001023858,0.0009295097,0.005005287,0.006593955,0.0798599,0.2782884,0.008702805,0.552042,0.0003336422],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.2023878,0.01228105,0.6052604,0.003752538,0.003731295,0.001390629,0.1032026,0.02513595,0.04285767],"genre_scores_gemma":[0.3719251,0.006174697,0.3978007,0.0005392427,0.001496921,0.0009086987,0.1460724,0.00269243,0.07238978],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.016006,"threshold_uncertainty_score":0.05354536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03865174747257399,"score_gpt":0.3454457365214059,"score_spread":0.3067939890488319,"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."}}