{"id":"W4388499228","doi":"10.1111/cobi.14221","title":"Combining camera trap surveys and IUCN range maps to improve knowledge of species distributions","year":2023,"lang":"en","type":"article","venue":"Conservation Biology","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of British Columbia","funders":"Norges Forskningsråd; Canada Research Chairs","keywords":"IUCN Red List; Camera trap; Geography; Range (aeronautics); Trap (plumbing); Cartography; Environmental science; Remote sensing; Ecology; Biology; Meteorology; Habitat; Engineering","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":[0.003505921,0.0004944368,0.000380944,0.005768904,0.0003051985,0.0009405297,0.000833118,0.0003174228,0.001699204],"category_scores_gemma":[0.01070315,0.0003502028,0.0002138488,0.00483023,0.0003306331,0.001613765,0.001241881,0.0003197765,0.0004519668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003887079,"about_ca_system_score_gemma":0.0003821368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01037149,"about_ca_topic_score_gemma":0.03899477,"domain_scores_codex":[0.9967,0.001427093,0.000253376,0.0006858879,0.0007774771,0.0001560254],"domain_scores_gemma":[0.9899579,0.002813635,0.004073292,0.001276896,0.001528155,0.0003501871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004483888,0.00005094797,0.8870965,0.0002045671,0.000198389,0.000125328,0.0005714686,0.001439096,0.00318306,0.0002623386,0.001990699,0.1048327],"study_design_scores_gemma":[0.000005971456,0.00005696626,0.9840091,0.00009105491,0.0000657358,0.0002959741,0.0007339968,0.008695327,0.001178172,0.0003638827,0.004478368,0.00002547587],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8902928,0.001744622,0.07657579,0.0002386289,0.0001186325,0.0005184413,0.007810166,0.0006110544,0.02208989],"genre_scores_gemma":[0.8985053,0.0004338312,0.0959971,0.00007341929,0.00004375007,0.0002896394,0.003729259,0.00004557729,0.0008823128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01037149,"threshold_uncertainty_score":0.02062225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02893498225094991,"score_gpt":0.2624476153838615,"score_spread":0.2335126331329116,"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."}}