{"id":"W2064662293","doi":"10.1111/jbi.12340","title":"Assessing coastal species distribution models through the integration of terrestrial, oceanic and atmospheric data","year":2014,"lang":"en","type":"article","venue":"Journal of Biogeography","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Environment and Climate Change Canada; University of British Columbia","funders":"Environment Canada; British Columbia Innovation Council","keywords":"Environmental science; Overfitting; Environmental data; Generalized additive model; Shore; Species distribution; Habitat; Physical geography; Scale (ratio); Terrestrial ecosystem; Ecology; Geography; Oceanography; Ecosystem; Cartography; Computer science; Statistics; Geology; Biology; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002198344,0.0007510444,0.0004164782,0.0006817082,0.0003722938,0.001121313,0.0008737964,0.0004665327,0.001641362],"category_scores_gemma":[0.005678856,0.0004448837,0.0007431588,0.0006183361,0.000244916,0.000908675,0.000831108,0.0006289288,0.0003243118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001072966,"about_ca_system_score_gemma":0.001087214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1096532,"about_ca_topic_score_gemma":0.0905105,"domain_scores_codex":[0.9996068,0.0001827036,0.00002089821,0.0001106158,0.00005071043,0.0000283348],"domain_scores_gemma":[0.9976221,0.001665091,0.000221827,0.00008959424,0.0002802848,0.0001211493],"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":[0.00007161607,0.00005408619,0.09426498,0.00002940946,0.0002116274,0.00005713239,0.00006329361,0.8920318,0.0005678949,0.0003691681,0.0003924282,0.01188659],"study_design_scores_gemma":[0.00000774081,0.00002556733,0.008873864,0.00001145113,0.00002627302,0.00001320989,0.00004421216,0.9903053,0.0001243006,0.0003651308,0.0001945937,0.000008339734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9569631,0.0002213314,0.03942458,0.0002659622,0.00001766682,0.0000357857,0.0007368455,0.0003259362,0.002008843],"genre_scores_gemma":[0.9868265,0.00007798893,0.01177233,0.00002991628,0.000008413091,0.00002190158,0.000632495,0.00003623455,0.0005942222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1096532,"threshold_uncertainty_score":0.2180299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06699555319766891,"score_gpt":0.2845224163470655,"score_spread":0.2175268631493966,"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."}}