{"id":"W3037801811","doi":"10.1002/ecs2.3160","title":"Making predictive modelling ART: accurate, reliable, and transparent","year":2020,"lang":"en","type":"article","venue":"Ecosphere","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"CLARITY; Computer science; Robustness (evolution); Predictive modelling; Process (computing); Field (mathematics); Risk analysis (engineering); Framing (construction); Data science; Management science; Machine learning; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00003450393,0.00008984654,0.00008917473,0.000001786574,0.00008757652,0.00003295857,0.0000843036,0.00003907401,0.09805133],"category_scores_gemma":[0.00000494698,0.0000852985,0.000025388,0.0001076582,0.00004800273,0.0001461354,0.00005764979,0.0000829203,0.00318085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009258657,"about_ca_system_score_gemma":0.000002756085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001443396,"about_ca_topic_score_gemma":0.00005585373,"domain_scores_codex":[0.9993594,0.000007198539,0.0001091628,0.0002283478,0.0001259003,0.0001699661],"domain_scores_gemma":[0.9997805,0.000008172937,0.00003274589,0.00007195838,0.000003796016,0.0001027672],"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":[0.000136228,0.00008855485,0.009759416,0.00005561493,0.00002836052,0.00002752457,0.002762342,0.1345494,0.0006062081,0.0008646871,0.848605,0.002516653],"study_design_scores_gemma":[0.0008361564,0.0002164157,0.04293203,0.00004237191,0.00003791427,0.000007998353,0.004025056,0.4262226,0.0006730504,0.0003184497,0.5242066,0.0004812879],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3147181,0.0002513638,0.01849465,0.001910967,0.00008736933,0.0002031914,0.00008037584,0.0001242252,0.6641297],"genre_scores_gemma":[0.998246,0.000165496,0.0002679793,0.0007195211,0.00002674074,0.00001042703,0.00001967796,0.000009397814,0.0005347466],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6835279,"threshold_uncertainty_score":0.9975953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08517083265199428,"score_gpt":0.2629929356105292,"score_spread":0.1778221029585349,"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."}}