{"id":"W4230775387","doi":"10.1515/iupac.81.0804","title":"Scope for Growth","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; Scope (computer science); Ecotoxicology; Relation (database); Environmental risk assessment; Ecology; Computer science; Data science; Risk assessment; Biology; Data mining; 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":["insufficient_payload"],"category_scores_codex":[0.004728894,0.001895457,0.002697,0.009313431,0.001859549,0.00931281,0.00462086,0.003009573,0.3178577],"category_scores_gemma":[0.05660451,0.0007575236,0.002636471,0.02194132,0.0007727874,0.006434209,0.005953129,0.003536348,0.2728919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003552658,"about_ca_system_score_gemma":0.009299331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02115738,"about_ca_topic_score_gemma":0.01987468,"domain_scores_codex":[0.9928933,0.001375772,0.001050936,0.002057105,0.00160088,0.001022086],"domain_scores_gemma":[0.981693,0.006210499,0.001398534,0.003257434,0.005543223,0.001897249],"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":[0.00007875155,0.00001046285,0.0009163301,0.001177024,0.00002803069,0.00001645495,0.00003310066,0.00008479774,0.00002356869,0.001850004,0.9871765,0.008604936],"study_design_scores_gemma":[0.00008693069,0.000008571514,0.002033904,0.001261133,0.00002449244,0.00004194291,0.0001410182,0.00009586837,0.00004794839,0.00192721,0.994311,0.00001999541],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000162271,0.0006424518,0.0001778093,0.0009246296,0.0003963721,0.00007783181,0.9886882,0.0003411708,0.008589256],"genre_scores_gemma":[0.001341401,0.0009749188,0.0009854456,0.0009478533,0.0001711748,0.000905937,0.9875475,0.0004826175,0.006643029],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6821423,"threshold_uncertainty_score":0.9729932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01519516970951068,"score_gpt":0.3972622206408587,"score_spread":0.382067050931348,"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."}}