{"id":"W2039951642","doi":"10.1111/2041-210x.12238","title":"Validation and calibration of probabilistic predictions in ecology","year":2014,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; McGill University","funders":"Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Probabilistic logic; Metric (unit); Calibration; Predictive modelling; Predictive power; Population viability analysis; Population; Statistical model; Computer science; Statistics; Goodness of fit; Ecology; Reliability (semiconductor); Econometrics; Machine learning; Artificial intelligence; Mathematics; Biology; Engineering; Power (physics)","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":[0.04449134,0.001183643,0.0009086559,0.00232246,0.0007321855,0.00217385,0.002566057,0.001787691,0.001412696],"category_scores_gemma":[0.1815961,0.0005410776,0.001314384,0.001427023,0.003151209,0.003332346,0.002956948,0.00265055,0.0003426727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001967929,"about_ca_system_score_gemma":0.001506091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00488351,"about_ca_topic_score_gemma":0.002065102,"domain_scores_codex":[0.9789548,0.01358039,0.0009540445,0.002438732,0.003431805,0.00064027],"domain_scores_gemma":[0.7036256,0.2520927,0.01416043,0.01896387,0.0103309,0.0008265629],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000179219,0.0001180858,0.04243059,0.00009169494,0.0001599521,0.00006917286,0.0002271299,0.9067414,0.0009605626,0.01560342,0.0005963529,0.03282239],"study_design_scores_gemma":[0.000006490097,0.00007417818,0.004289347,0.00003537977,0.000009010118,0.00003479238,0.00003635194,0.9801144,0.0008257688,0.01432144,0.0002328678,0.00001998833],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4243669,0.000258475,0.5694461,0.0005305799,0.00006211291,0.0001299051,0.0003669274,0.000703493,0.004135438],"genre_scores_gemma":[0.9632528,0.00004142052,0.03593326,0.00006915806,0.00002485733,0.00008982031,0.0003484811,0.00005555263,0.0001846452],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9555086,"threshold_uncertainty_score":0.2352955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0266500800654496,"score_gpt":0.313788431909609,"score_spread":0.2871383518441594,"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."}}