{"id":"W4256713921","doi":"10.1515/iupac.81.0645","title":"Octanol-Air Partition Coefficient","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Partition coefficient; Partition (number theory); Mathematics; Environmental science; Chemistry; Chromatography; Combinatorics","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":[0.0007783949,0.001531219,0.001682999,0.002176436,0.0005304795,0.001942764,0.001902682,0.001215108,0.02783902],"category_scores_gemma":[0.003950968,0.0003893124,0.001533267,0.003565596,0.0002551322,0.001631485,0.001067852,0.0017588,0.0430532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001470335,"about_ca_system_score_gemma":0.001493041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01774819,"about_ca_topic_score_gemma":0.02488044,"domain_scores_codex":[0.9990146,0.0001207409,0.0001375701,0.0003966547,0.0002408002,0.00008963463],"domain_scores_gemma":[0.9985672,0.0004310713,0.000267171,0.0002505066,0.0004101272,0.00007398094],"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":[0.0005321997,0.0001119593,0.01236221,0.003635556,0.0002362217,0.00005847454,0.00004680663,0.001524482,0.0009316968,0.0009845025,0.9614986,0.0180773],"study_design_scores_gemma":[0.0005835769,0.00008548155,0.04294582,0.000565275,0.0002158468,0.0002788049,0.0001085326,0.001938839,0.002482867,0.003289028,0.94741,0.00009597687],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008021258,0.0003651484,0.0001045175,0.00005770739,0.00002448094,0.00001156052,0.997611,0.0001962893,0.0008271186],"genre_scores_gemma":[0.002663214,0.0003689064,0.0003688607,0.00007330569,0.00001074147,0.0000700035,0.9954973,0.00004903484,0.0008986541],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02783902,"threshold_uncertainty_score":0.09313083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01201528434649955,"score_gpt":0.3510475796813665,"score_spread":0.3390322953348669,"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."}}