{"id":"W2941774662","doi":"10.1002/ecm.1370","title":"A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels","year":2019,"lang":"en","type":"article","venue":"Ecological Monographs","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":558,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Helsingin Yliopiston Tiedesäätiö; Jane ja Aatos Erkon Säätiö; Academy of Finland; Norges Forskningsråd; Helsingin Yliopisto; Ministerio de Ciencia, Innovación y Universidades; National Institute for Mathematical and Biological Synthesis","keywords":"Species richness; Extrapolation; Context (archaeology); Predictive power; Calibration; Predictive modelling; Species distribution; Ecology; Interpolation (computer graphics); Computer science; Contrast (vision); Machine learning; Econometrics; Statistics; Artificial intelligence; Mathematics; Biology; Habitat","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":[0.01979167,0.001936186,0.001310238,0.003554635,0.0006302341,0.001727955,0.001938681,0.001371327,0.0008067879],"category_scores_gemma":[0.02228455,0.0006605097,0.002042388,0.002051231,0.0007418855,0.002016218,0.001706268,0.001481919,0.0003385836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001842904,"about_ca_system_score_gemma":0.001884614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01717383,"about_ca_topic_score_gemma":0.01007852,"domain_scores_codex":[0.997209,0.001513104,0.0001969694,0.0005126381,0.0004395366,0.000128744],"domain_scores_gemma":[0.9789882,0.01642901,0.001227918,0.001256961,0.001609776,0.0004880432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004040767,0.0002408975,0.06170952,0.0001741805,0.0006140676,0.0000714962,0.0001227033,0.8860065,0.001046986,0.001194583,0.001129174,0.04728573],"study_design_scores_gemma":[0.00001659037,0.0001286895,0.00488894,0.00003323757,0.00004415105,0.00002386404,0.00003950611,0.9930236,0.0006336374,0.0009472636,0.0002001224,0.00002031431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9089204,0.002312903,0.0818655,0.0008505125,0.00006924665,0.0001312396,0.001788262,0.001577997,0.002483921],"genre_scores_gemma":[0.9702752,0.0003658104,0.02743609,0.00008725303,0.00002532282,0.00007128111,0.001438264,0.00005286071,0.0002479972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01979167,"threshold_uncertainty_score":0.1046696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1041770819909598,"score_gpt":0.2680008263131924,"score_spread":0.1638237443222326,"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."}}