{"id":"W4232648183","doi":"10.1515/iupac.76.0113","title":"Adsorption Factor","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"History and advancements in chemistry","field":"Chemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.001181961,0.002251563,0.001571678,0.004393949,0.000969994,0.003276276,0.002384701,0.001677564,0.1150213],"category_scores_gemma":[0.009329904,0.0006659603,0.002317415,0.005999926,0.0003696721,0.002688755,0.001755773,0.001768063,0.1508914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001604351,"about_ca_system_score_gemma":0.002402175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02023179,"about_ca_topic_score_gemma":0.03430007,"domain_scores_codex":[0.9981549,0.0002288646,0.0002981384,0.0007399353,0.0004062612,0.0001718719],"domain_scores_gemma":[0.9967343,0.001052333,0.0003338095,0.0007906083,0.0009015384,0.000187515],"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":[0.0001505862,0.00004572307,0.00307371,0.001805386,0.00007217767,0.00002828012,0.00004068145,0.000438299,0.0002950146,0.001048729,0.9816867,0.01131478],"study_design_scores_gemma":[0.0001091059,0.00002301773,0.006038346,0.0004305943,0.00005234204,0.00006603019,0.00007673637,0.0004223291,0.0004245971,0.001592201,0.9907261,0.0000386319],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002225119,0.0002109223,0.0001758058,0.00006568837,0.00003784103,0.00002198548,0.9972705,0.0005342332,0.001460441],"genre_scores_gemma":[0.0006346938,0.00015594,0.000617483,0.00008296612,0.00001126356,0.0001017996,0.9968297,0.0001224974,0.001443706],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1150213,"threshold_uncertainty_score":0.3847845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01671142029728642,"score_gpt":0.3867484431175165,"score_spread":0.3700370228202301,"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."}}