{"id":"W4243125888","doi":"10.1515/iupac.88.0872","title":"Grey Matter","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Relation (database); Computer science; Linguistics; Philosophy; Data mining","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006336598,0.0003428346,0.0003941463,0.00004116971,0.0003894341,0.0001494309,0.0007822507,0.0002931667,0.01991211],"category_scores_gemma":[0.0003213325,0.0003046709,0.0001302124,0.00005578333,0.0003123039,0.0001542618,0.0005946464,0.0005626413,0.0002908298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004493444,"about_ca_system_score_gemma":0.00005729005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00137974,"about_ca_topic_score_gemma":0.0005159494,"domain_scores_codex":[0.9976428,0.00006014099,0.0003263978,0.000516946,0.001016656,0.0004370541],"domain_scores_gemma":[0.9982607,0.00004055458,0.0003348891,0.001166656,0.00003086847,0.0001663553],"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":[0.00002046642,0.00006963876,0.001752739,0.00004260679,0.00001833461,0.00003939135,0.00001789018,0.00001289741,0.000002396764,1.065272e-7,0.9944271,0.003596468],"study_design_scores_gemma":[0.0002070668,0.00005927068,0.002748887,0.0001481498,0.00004765634,0.00001344449,0.00001171888,0.00001214015,0.00000620413,0.00005773752,0.9963211,0.0003666701],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009639573,0.00005725059,0.0000375717,0.0003498399,0.001040675,0.0001314041,0.9967553,0.00004045947,0.0006235631],"genre_scores_gemma":[0.000111214,0.00004580561,0.000148992,0.0002210362,0.000939421,0.000007394151,0.9954432,0.00003093001,0.003051985],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01962128,"threshold_uncertainty_score":0.9999405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02521617823231041,"score_gpt":0.4125023845337802,"score_spread":0.3872862063014698,"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."}}