{"id":"W3025488901","doi":"10.1002/jwmg.21879","title":"Random Encounter and Staying Time Model Testing with Human Volunteers","year":2020,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of Alberta","funders":"","keywords":"Wildlife; Camera trap; Rest (music); Population; Computer science; Abundance (ecology); Range (aeronautics); Statistical model; Statistics; Geography; Ecology; Artificial intelligence; Mathematics; Demography; Biology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.05659181,0.001024381,0.001346608,0.001072041,0.0007573366,0.001158886,0.004089394,0.001283961,0.003765866],"category_scores_gemma":[0.1073318,0.0004504588,0.002459218,0.0006577637,0.002248642,0.001833875,0.001608971,0.002090686,0.0004891225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00100849,"about_ca_system_score_gemma":0.001348284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006552664,"about_ca_topic_score_gemma":0.003001721,"domain_scores_codex":[0.9716417,0.02253537,0.0008147994,0.002580476,0.001443094,0.0009845857],"domain_scores_gemma":[0.7049245,0.2688896,0.01035608,0.009803935,0.004223848,0.001802103],"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":[0.006332143,0.001616556,0.2604633,0.0002763504,0.001547381,0.0008570886,0.001085332,0.6392979,0.001883723,0.02777025,0.002972535,0.05589743],"study_design_scores_gemma":[0.00009048286,0.001040337,0.00674717,0.0000167819,0.0000870225,0.0001042032,0.0001705609,0.9860924,0.0007102593,0.004639606,0.0002758431,0.00002538781],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8361584,0.0001200566,0.1602184,0.0002911893,0.0001217632,0.000382199,0.000506844,0.0002928784,0.001908238],"genre_scores_gemma":[0.973343,0.00002478137,0.0246689,0.00009133521,0.00002930934,0.0003185533,0.0003789709,0.00003229173,0.001112865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05659181,"threshold_uncertainty_score":0.2992896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620698206691736,"score_gpt":0.2070056227639558,"score_spread":0.1907986406970385,"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."}}