{"id":"W2792790256","doi":"10.1002/ecs2.2092","title":"Animal movement affects interpretation of occupancy models from camera‐trap surveys of unmarked animals","year":2018,"lang":"en","type":"article","venue":"Ecosphere","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":128,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Alberta; Natural Resources Canada; University of British Columbia; Canadian Forest Service","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Occupancy; Sampling (signal processing); Statistics; Population; Camera trap; Distance sampling; Population density; Abundance (ecology); Habitat; Ecology; Environmental science; Mathematics; Biology; Computer science; Computer vision; Demography","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":[0.007436154,0.0004356351,0.0004808815,0.0005319838,0.0003202042,0.001419064,0.001390563,0.0006164411,0.0008501257],"category_scores_gemma":[0.03449422,0.000535088,0.0008664007,0.0003757136,0.000816195,0.001085535,0.0007794356,0.0007651876,0.0001595245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001081247,"about_ca_system_score_gemma":0.0005907512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0075229,"about_ca_topic_score_gemma":0.005868278,"domain_scores_codex":[0.9964257,0.002353427,0.0002401991,0.0005618841,0.0003057281,0.0001131226],"domain_scores_gemma":[0.9687053,0.02593633,0.002114306,0.001876103,0.001071604,0.0002963997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004349397,0.0000818623,0.2145119,0.0001637561,0.0003838026,0.0002301541,0.0005447057,0.7521858,0.006176366,0.00400871,0.0006236014,0.02065434],"study_design_scores_gemma":[0.00002327773,0.00007970208,0.03068166,0.00003871149,0.00005146597,0.0001701047,0.0001285217,0.9633541,0.001871797,0.003132198,0.0004348631,0.00003346911],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7548373,0.0001127484,0.2424847,0.0001846756,0.00003247691,0.00004379671,0.0002099337,0.0004525459,0.001641723],"genre_scores_gemma":[0.9864303,0.00002721511,0.01319155,0.00004077022,0.000005829473,0.00002674263,0.0001373758,0.00006341743,0.00007675141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0075229,"threshold_uncertainty_score":0.03932661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01124469807008595,"score_gpt":0.2282246226383722,"score_spread":0.2169799245682862,"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."}}