{"id":"W4243719173","doi":"10.1515/iupac.76.0239","title":"Fractionation","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Scientific Measurement and Uncertainty Evaluation","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Toxicokinetics; CAS Registry Number; Relation (database); Computer science; Toxicology; Medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01480048,0.0003055101,0.0004769597,0.0008500973,0.000276046,0.0004985947,0.001138594,0.0003213251,0.04990909],"category_scores_gemma":[0.02517797,0.0001885826,0.0002296449,0.0008082674,0.0001344696,0.0004030563,0.0001342609,0.0002930266,0.0001189159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00061819,"about_ca_system_score_gemma":0.001245609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004640438,"about_ca_topic_score_gemma":0.00100856,"domain_scores_codex":[0.9849889,0.0004232915,0.001074685,0.0009338459,0.01224882,0.0003305074],"domain_scores_gemma":[0.9920317,0.0009303735,0.000871386,0.001407326,0.004596975,0.0001622746],"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":[0.00006103417,0.00007775817,0.00001614274,0.000004561995,0.00002234894,0.000002880774,0.000008897931,0.000009071265,0.00002275662,0.00002013932,0.9649237,0.03483068],"study_design_scores_gemma":[0.000526797,0.00005996466,0.0001585283,0.00007397529,0.00004583772,0.000002728126,0.00004539532,0.00009109199,0.00002216071,0.008434718,0.9902843,0.0002544554],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005806455,0.0001599736,0.0026064,0.002438455,0.005417231,0.0003185541,0.9885817,0.00004176443,0.0003778587],"genre_scores_gemma":[0.0000554946,0.00007152259,0.00004032454,0.0002743082,0.001265608,0.00001397038,0.9919069,0.00001286261,0.006358955],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04979017,"threshold_uncertainty_score":0.9830334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1768045266930182,"score_gpt":0.5536987334343827,"score_spread":0.3768942067413645,"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."}}