{"id":"W2782461164","doi":"","title":"Computing EFMs consistent with equilibrium constants","year":2017,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science","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":[0.002079052,0.001119637,0.001325597,0.001803315,0.0009163998,0.002385496,0.002346177,0.002686833,0.01879706],"category_scores_gemma":[0.02359605,0.0007312974,0.0009047743,0.001362788,0.001908014,0.004508095,0.002784981,0.002949592,0.00153851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001106266,"about_ca_system_score_gemma":0.0009300665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008755361,"about_ca_topic_score_gemma":0.00125437,"domain_scores_codex":[0.9993223,0.0002637993,0.00003496441,0.0001729259,0.000119888,0.00008609471],"domain_scores_gemma":[0.9903144,0.007768552,0.0004197265,0.0007243467,0.0004685488,0.0003043635],"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":[0.0002671368,0.0001293979,0.0008015324,0.0004093277,0.00009395798,0.0001549965,0.0001294894,0.2859361,0.001679261,0.6613495,0.005641879,0.04340738],"study_design_scores_gemma":[0.00003550454,0.00001502277,0.00006796292,0.00002287075,0.00001136524,0.00001978479,0.00003098434,0.4182175,0.0005200545,0.5802798,0.0007709498,0.000008258618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1129001,0.0009025447,0.8471621,0.002126497,0.0004036195,0.0001021761,0.0005328303,0.00135221,0.03451791],"genre_scores_gemma":[0.7837282,0.0004795814,0.1998426,0.0006011089,0.0003428784,0.0002201607,0.0008178661,0.0008060178,0.01316158],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01879706,"threshold_uncertainty_score":0.06288248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02109923452714824,"score_gpt":0.2413523265706921,"score_spread":0.2202530920435439,"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."}}