{"id":"W4235374965","doi":"10.1515/iupac.76.0202","title":"Diffusion","year":2016,"lang":"fr","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; Philosophy; Linguistics","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":[0.002019719,0.002251094,0.001577548,0.004050997,0.001346621,0.005004836,0.003792185,0.002146157,0.1619144],"category_scores_gemma":[0.01496603,0.0008398631,0.002501333,0.006791559,0.0005011859,0.004716961,0.0033598,0.002488846,0.2613765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001675106,"about_ca_system_score_gemma":0.00276364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02073593,"about_ca_topic_score_gemma":0.03745918,"domain_scores_codex":[0.9973253,0.0005135976,0.0003849766,0.001014977,0.000510923,0.0002502228],"domain_scores_gemma":[0.9952934,0.00139475,0.0003473777,0.001646835,0.001049342,0.00026821],"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":[0.00008073399,0.00002031251,0.0007941331,0.0006561809,0.00003428708,0.00001729921,0.00004935678,0.0003369355,0.00007852569,0.001450607,0.9875732,0.008908533],"study_design_scores_gemma":[0.0000930793,0.00001329406,0.001385505,0.0002923319,0.00002049546,0.00004367943,0.0001000551,0.0006201153,0.0001786341,0.003185672,0.9940385,0.00002878089],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002607701,0.0002224917,0.0004850386,0.0002873989,0.00009619939,0.00004409549,0.9917098,0.002532328,0.004361906],"genre_scores_gemma":[0.0006379429,0.0001698955,0.001200617,0.0001642392,0.00002004426,0.0001646746,0.9939942,0.0004831576,0.003165364],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8380857,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01491157796993549,"score_gpt":0.403734606703148,"score_spread":0.3888230287332126,"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."}}