{"id":"W4390835141","doi":"10.13140/rg.2.2.33309.38881","title":"CALCULER LA SÉMANTIQUE AVEC IEML","year":2023,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Data science; Natural language processing; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004773589,0.001957566,0.001110538,0.003280228,0.001774911,0.01233563,0.001906087,0.001842549,0.01097623],"category_scores_gemma":[0.01501885,0.001183475,0.003917005,0.002557519,0.004692088,0.01121383,0.003601915,0.006267642,0.004960491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003142055,"about_ca_system_score_gemma":0.003342662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01185422,"about_ca_topic_score_gemma":0.009402231,"domain_scores_codex":[0.9923629,0.002490739,0.0005820487,0.001205583,0.003011644,0.0003470904],"domain_scores_gemma":[0.9956198,0.002192514,0.0002225566,0.000979041,0.0008592356,0.0001267696],"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":[0.0001518996,0.0001005599,0.0008739185,0.0003963768,0.0001445819,0.0004242602,0.001024091,0.02048962,0.004950758,0.8567978,0.008309999,0.1063361],"study_design_scores_gemma":[0.0001447069,0.00008601127,0.0006832842,0.0002256154,0.00009894645,0.0006046292,0.0007707961,0.1761903,0.01958551,0.5222175,0.2792304,0.0001623538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004245604,0.000263379,0.9801052,0.0007675444,0.000294422,0.00008655536,0.0004551311,0.004414921,0.009367255],"genre_scores_gemma":[0.07787951,0.001051398,0.9065961,0.0004576988,0.0003266926,0.000293557,0.00200753,0.002026638,0.00936094],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01233563,"threshold_uncertainty_score":0.03671914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03330757558379217,"score_gpt":0.2537473511467099,"score_spread":0.2204397755629177,"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."}}