{"id":"W2105663561","doi":"10.1111/j.1600-0587.2011.07147.x","title":"Method selection for species distribution modelling: are temporally or spatially independent evaluations necessary?","year":2011,"lang":"en","type":"article","venue":"Ecography","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Species distribution; Context (archaeology); Environmental niche modelling; Habitat; Model selection; Selection (genetic algorithm); Ecology; Physical geography; Climate change; Computer science; Statistics; Environmental science; Geography; Machine learning; Mathematics; Biology; Ecological niche","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":[],"category_scores_codex":[0.0004460568,0.0001713226,0.000159321,0.00006351808,0.0003201778,0.00005180623,0.0001918296,0.0001048167,0.03826021],"category_scores_gemma":[0.00003385407,0.0001493095,0.0001720364,0.0005934378,0.00008644613,0.0002684574,0.00006096175,0.00009712905,0.0001958273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002384802,"about_ca_system_score_gemma":0.00001704396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004343662,"about_ca_topic_score_gemma":0.003112586,"domain_scores_codex":[0.9986448,0.00006848641,0.0002732951,0.0003712513,0.0003354279,0.000306694],"domain_scores_gemma":[0.999411,0.00004441986,0.000192364,0.0001735338,0.00006559503,0.0001130303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008795231,0.001587416,0.891916,0.00006541536,0.0002246173,0.000006179615,0.001497385,0.005827039,0.002475788,0.008218579,0.07971139,0.007590665],"study_design_scores_gemma":[0.001248725,0.000464097,0.8861099,0.00001995498,0.0001564509,0.00001335872,0.001745808,0.02860183,0.006088893,0.004846207,0.07007363,0.0006311493],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2099264,0.00002622104,0.7716829,0.0002932146,0.0002812474,0.000838835,0.0007295842,0.0001762916,0.01604531],"genre_scores_gemma":[0.979586,0.00008353948,0.01811153,0.0001713332,0.00008488401,0.000285064,0.0007854037,0.000025465,0.0008667868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7696596,"threshold_uncertainty_score":0.9626189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.11836073344475,"score_gpt":0.3096503405027658,"score_spread":0.1912896070580158,"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."}}