{"id":"W4254850371","doi":"10.1515/iupac.81.0487","title":"Interference Competition","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Merger and Competition Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Ecotoxicology; Environmental risk assessment; Computer science; Ecology; Risk assessment; 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":[0.001194316,0.001979187,0.001582161,0.004356959,0.001225488,0.004093939,0.003927197,0.001993971,0.1127965],"category_scores_gemma":[0.01007281,0.0006792151,0.002140946,0.008227623,0.0004460415,0.002673644,0.002045171,0.00246096,0.08663882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002859089,"about_ca_system_score_gemma":0.002889533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05140167,"about_ca_topic_score_gemma":0.07182101,"domain_scores_codex":[0.9978523,0.0003120714,0.0002165388,0.0005736153,0.0006719113,0.0003735423],"domain_scores_gemma":[0.9964054,0.0009122046,0.0006650073,0.0007500402,0.0009678429,0.0002995804],"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":[0.00009849571,0.00005234062,0.00535764,0.0003977871,0.00005481029,0.00004796321,0.00002405485,0.001071535,0.00005717939,0.004503128,0.9814683,0.006866735],"study_design_scores_gemma":[0.0002140383,0.00003088355,0.01431358,0.0003024322,0.0000463878,0.0002180557,0.000135048,0.002561683,0.0002569084,0.00776418,0.9741145,0.00004228429],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001200693,0.0003628544,0.0003667082,0.0002629884,0.0000683425,0.00002947217,0.9888785,0.0004049487,0.008425445],"genre_scores_gemma":[0.003912694,0.0002337868,0.0005613968,0.0001624357,0.00003063005,0.0001026312,0.9896316,0.00009139397,0.005273458],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1127965,"threshold_uncertainty_score":0.3773418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02486167773955355,"score_gpt":0.3360436401985673,"score_spread":0.3111819624590137,"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."}}