{"id":"W4244088976","doi":"10.1515/iupac.76.0389","title":"Stochastic","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Toxicokinetics; Relation (database); Hazard; Computer science; Toxicology; Medicine; Chemistry; Pharmacology; Data mining; Biology; Linguistics; Philosophy","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001387691,0.00232546,0.001387918,0.003372414,0.0009941848,0.003272696,0.002949264,0.001854848,0.1355606],"category_scores_gemma":[0.0109553,0.0006905984,0.002132001,0.005185603,0.0004442139,0.002814384,0.002394799,0.002013688,0.1968115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001615385,"about_ca_system_score_gemma":0.002490249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0172034,"about_ca_topic_score_gemma":0.035983,"domain_scores_codex":[0.9978188,0.0004055244,0.0003092645,0.0007822813,0.0004804714,0.0002037088],"domain_scores_gemma":[0.9964037,0.001077954,0.0003134263,0.00109967,0.0008894561,0.0002158491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006429156,0.00002015787,0.0008694606,0.0005702078,0.00002967344,0.00001821679,0.00002210955,0.0003948507,0.00008545139,0.0009752119,0.9902748,0.006675649],"study_design_scores_gemma":[0.0001233025,0.00001869392,0.002086243,0.0003700937,0.00002508333,0.00007006767,0.0000707814,0.0009417092,0.0002166865,0.003391068,0.9926576,0.0000286746],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001731652,0.0001459838,0.0003238068,0.0001331377,0.00005536106,0.00002452419,0.9965507,0.000806783,0.001786495],"genre_scores_gemma":[0.0003894305,0.0001006939,0.0006365219,0.0001045698,0.00001139689,0.0000917526,0.997391,0.0001230052,0.001151531],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8644394,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01666205318789033,"score_gpt":0.417833395797886,"score_spread":0.4011713426099957,"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."}}