{"id":"W3048448154","doi":"10.1111/mam.12210","title":"Modelling the spatial distribution of selected North American woodland mammals under future climate scenarios","year":2020,"lang":"en","type":"article","venue":"Mammal Review","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Woodland; Climate change; Range (aeronautics); Species distribution; Biodiversity; Ecology; Geography; Habitat; Climate model; Environmental niche modelling; Ecological niche; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0001527069,0.0001556569,0.0002986271,0.000004217241,0.0001285008,0.00002035503,0.0002494248,0.00002645148,0.006108718],"category_scores_gemma":[0.00003757963,0.0001042357,0.0001008467,0.0007647916,0.0001665591,0.00006575367,0.0001441357,0.0001333534,0.000377759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001337523,"about_ca_system_score_gemma":0.00001090283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00031579,"about_ca_topic_score_gemma":0.0004280638,"domain_scores_codex":[0.9987147,0.00009547204,0.0003185791,0.0002499506,0.000322446,0.0002987928],"domain_scores_gemma":[0.9994094,0.00002390269,0.0002144206,0.0001983367,0.00002956764,0.0001243197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007774895,0.0008710398,0.5265455,0.007182817,0.0003126409,0.00006109227,0.001664942,0.06664858,0.001655827,0.004939654,0.1503168,0.2390235],"study_design_scores_gemma":[0.0005342777,0.000304117,0.4460796,0.0003126492,0.0002670985,0.00001624542,0.0005950881,0.02187724,0.0001378422,0.00001128991,0.5292675,0.0005970683],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9667329,0.005372713,0.007534554,0.017174,0.0001039199,0.0009229218,0.0006666023,0.00009372264,0.001398657],"genre_scores_gemma":[0.9638699,0.03357896,0.0000271189,0.001901704,0.00009353516,0.00002192344,0.0004815901,0.00001137894,0.00001385111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3789506,"threshold_uncertainty_score":0.9947999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02727837059070901,"score_gpt":0.2419833700221781,"score_spread":0.2147049994314691,"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."}}