{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06436146,0.0015778,0.001912579,0.003899934,0.002019938,0.01663679,0.004333398,0.00574648,0.003625823],"category_scores_gemma":[0.1520949,0.001447019,0.001664991,0.003253782,0.01441964,0.02311702,0.007979591,0.008932161,0.001413749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004676568,"about_ca_system_score_gemma":0.009730841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004728966,"about_ca_topic_score_gemma":0.00278903,"domain_scores_codex":[0.9532173,0.02641304,0.002482015,0.002393525,0.01449336,0.001000804],"domain_scores_gemma":[0.8813078,0.07992157,0.006510221,0.01668006,0.01442079,0.001159565],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003174641,0.00005999246,0.001067337,0.0008447373,0.0001106054,0.00009678447,0.0007652055,0.05365951,0.0005612613,0.8452713,0.01286342,0.08466805],"study_design_scores_gemma":[0.00001562947,0.00003199496,0.0004978665,0.002003446,0.00007053032,0.00006352801,0.000461344,0.0530296,0.001329165,0.8777131,0.06468799,0.00009571827],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00801078,0.01860544,0.8719017,0.07219421,0.002008104,0.0002545707,0.000456528,0.0009618286,0.02560685],"genre_scores_gemma":[0.3877743,0.03632313,0.5629884,0.004745139,0.002967558,0.0006735348,0.0007682437,0.0007620421,0.002997613],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9356385,"threshold_uncertainty_score":0.34038,"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."}}