{"id":"W3099535352","doi":"10.3166/ria.32.287-312","title":"SMILK, trait d’union entre langue naturelle et données sur le web","year":2018,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Annotation; World Wide Web; Knowledge base; Ontology; Population; Field (mathematics); Information retrieval; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001216105,0.0004402564,0.0004806616,0.0001960491,0.0005173017,0.0004263961,0.001590925,0.0004077472,0.001021165],"category_scores_gemma":[0.0003960827,0.0004443131,0.0002792147,0.0009704159,0.0007108308,0.0008322339,0.00056609,0.0005836566,0.003086773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007774041,"about_ca_system_score_gemma":0.0004608148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001941678,"about_ca_topic_score_gemma":0.002967446,"domain_scores_codex":[0.9964239,0.0003972233,0.0007058868,0.001026998,0.0003711151,0.001074871],"domain_scores_gemma":[0.997373,0.000517357,0.0002377264,0.001213153,0.0004354538,0.000223265],"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":[0.00004397162,0.001115035,0.0006345154,0.0003740225,0.0001069738,0.000283441,0.02188872,0.003108391,0.008654783,0.6149445,0.08960693,0.2592387],"study_design_scores_gemma":[0.0001490354,0.0003651438,0.0005478507,0.0005922568,0.00003367602,0.0002126339,0.002901708,0.4421298,0.09125002,0.008997015,0.4521607,0.0006602191],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08378632,0.03255293,0.6308555,0.1145805,0.01610542,0.0009472716,0.00007905594,0.000806298,0.1202867],"genre_scores_gemma":[0.9464939,0.00120858,0.007144144,0.001234825,0.0009098716,0.00001549993,0.00001348025,0.00003956582,0.0429401],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8627076,"threshold_uncertainty_score":0.9998921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06753888455575599,"score_gpt":0.2733073391012237,"score_spread":0.2057684545454677,"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."}}