{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005154651,0.0001242252,0.0002473155,0.0000257928,0.0001801366,0.000008377217,0.0001564434,0.00009332076,0.01769831],"category_scores_gemma":[0.00005000543,0.0001018918,0.00007599179,0.0002492842,0.000654452,0.0001879078,0.0003417912,0.000157763,0.0000483244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003251535,"about_ca_system_score_gemma":0.000005550754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003438149,"about_ca_topic_score_gemma":0.00005668025,"domain_scores_codex":[0.9986774,0.0002834095,0.0002640167,0.0001735494,0.0004193549,0.0001822981],"domain_scores_gemma":[0.9992767,0.0001759022,0.0001822414,0.0002150871,0.00009300969,0.00005707677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002081463,0.0005702222,0.974698,0.00005948923,0.00003791578,2.387469e-7,0.0009581661,0.002934038,0.01773387,0.001251852,0.001042666,0.0005053812],"study_design_scores_gemma":[0.0005497527,0.0006300544,0.982498,0.00001109892,0.00002962085,0.000001664591,0.002652439,0.008804587,0.003562755,0.0005599516,0.0005983989,0.0001016822],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798615,0.00005090955,0.00002832965,0.00003351479,0.0000604364,0.0004395748,0.0008426416,0.00001979834,0.01866332],"genre_scores_gemma":[0.9994455,0.0001685335,0.0000216188,0.00002374535,0.0000040148,0.00002268671,0.0002096548,0.000003361915,0.0001008741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01958403,"threshold_uncertainty_score":0.9831997,"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."}}