{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006667677,0.001201773,0.001450971,0.003163059,0.002391144,0.007895133,0.001184059,0.001926398,0.02302645],"category_scores_gemma":[0.03312733,0.0009425486,0.001578354,0.002811829,0.001981503,0.009912626,0.003663787,0.004050739,0.01224605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003389178,"about_ca_system_score_gemma":0.003200239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01471629,"about_ca_topic_score_gemma":0.01096104,"domain_scores_codex":[0.9923579,0.001934262,0.0007893757,0.001304834,0.003143031,0.0004705174],"domain_scores_gemma":[0.9852744,0.006911939,0.0004455328,0.00251091,0.004153284,0.0007039272],"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":[0.001371961,0.0002045221,0.003252709,0.0009180752,0.0001978744,0.0006663793,0.001097275,0.004904993,0.01499732,0.2546725,0.3546831,0.3630331],"study_design_scores_gemma":[0.0001403558,0.00009581706,0.004127113,0.0003946983,0.0001115673,0.000899577,0.0004731585,0.05716213,0.02329019,0.07862857,0.8344938,0.0001830345],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04866805,0.01198707,0.6657012,0.03610172,0.01088138,0.0003697297,0.01534968,0.05796517,0.152976],"genre_scores_gemma":[0.3902801,0.00813479,0.2720515,0.003518042,0.004604744,0.0006302131,0.03883766,0.02162307,0.2603198],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02302645,"threshold_uncertainty_score":0.0770312,"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."}}