{"id":"W2286748342","doi":"10.1111/gcb.13251","title":"Benchmarking novel approaches for modelling species range dynamics","year":2016,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":232,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski","funders":"Research Executive Agency; European Research Council; Natural Sciences and Engineering Research Council of Canada; Seventh Framework Programme; Natur og Univers, Det Frie Forskningsråd; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Deutsche Forschungsgemeinschaft","keywords":"Benchmarking; Climate change; Biological dispersal; Computer science; Range (aeronautics); Population; Species distribution; Extinction (optical mineralogy); Population model; Environmental niche modelling; Ecology; Biology; Engineering; Habitat","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":[],"consensus_categories":[],"category_scores_codex":[0.005208058,0.001352797,0.001021798,0.001898465,0.0004707982,0.001952887,0.003504841,0.001787248,0.002339355],"category_scores_gemma":[0.01283788,0.000598099,0.001705328,0.001758759,0.0008936095,0.003090426,0.001902197,0.001824124,0.0003776439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001816994,"about_ca_system_score_gemma":0.001390922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0150635,"about_ca_topic_score_gemma":0.009542241,"domain_scores_codex":[0.9979394,0.0008937535,0.0001806163,0.0004586774,0.0004191456,0.000108483],"domain_scores_gemma":[0.9938723,0.004189321,0.0004611707,0.0007626763,0.000536164,0.0001784521],"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":[0.00001809187,0.00002992961,0.002467262,0.00007930796,0.00006873345,0.00002419068,0.00003556783,0.9838762,0.000299639,0.004424981,0.0001548005,0.008521308],"study_design_scores_gemma":[0.000005545184,0.00001725908,0.000360639,0.00001289122,0.000008287426,0.0000109102,0.00001289978,0.9942585,0.0002269947,0.004651794,0.0004269941,0.000007265618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1936809,0.002241218,0.7907189,0.0009387175,0.0001791276,0.0002025698,0.001672941,0.001512289,0.008853359],"genre_scores_gemma":[0.7615596,0.001431444,0.2330133,0.0002535224,0.0001014144,0.0003998404,0.002207582,0.0002731458,0.0007601594],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0150635,"threshold_uncertainty_score":0.02995163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.184808578803956,"score_gpt":0.2736876766434665,"score_spread":0.08887909783951059,"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."}}