{"id":"W7100672429","doi":"","title":"PHILIPPE PASQUIER ASPECTS COGNITIFS DES DIALOGUES ENTRE AGENTS ARTIFICIELS L'approche par la cohérence cognitive","year":2015,"lang":"en","type":"article","venue":"","topic":"Microbial Metabolism and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cognition; Context (archaeology)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002039638,0.0002072929,0.0001743063,0.00003786952,0.0001194248,0.00006871359,0.00020111,0.0001607804,0.0001430423],"category_scores_gemma":[0.0003152628,0.0001840111,0.0000778473,0.0001586549,0.0003405938,0.00000854289,0.0001465489,0.00009501756,0.0003682701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000765022,"about_ca_system_score_gemma":0.0001253139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004481691,"about_ca_topic_score_gemma":0.0001173243,"domain_scores_codex":[0.9987701,0.0001068317,0.000217208,0.0004502457,0.0001387701,0.0003168395],"domain_scores_gemma":[0.999098,0.00003799141,0.0000781163,0.0002728427,0.0002861186,0.000226957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001219077,0.0005009404,0.001493747,0.0000264511,0.0001250365,0.000006244193,0.001262082,0.000006471222,0.9472608,0.001213596,0.03435876,0.01362399],"study_design_scores_gemma":[0.001242179,0.0001225802,0.002603682,0.00004075554,0.0001223168,0.00003760457,0.0009732296,0.00001242793,0.6894923,0.003688954,0.3011457,0.0005182866],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9330091,0.0005617841,0.0115849,0.000124859,0.0001158857,0.0004177279,0.000176147,0.00004826294,0.05396135],"genre_scores_gemma":[0.9938195,0.0002055281,0.001283751,0.0005105752,0.0002786435,0.00008671473,0.0004248414,0.00002428429,0.003366148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.266787,"threshold_uncertainty_score":0.7503753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1350126061122092,"score_gpt":0.3369654754137914,"score_spread":0.2019528693015822,"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."}}