{"id":"W3087111629","doi":"10.1002/ece3.6797","title":"An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou","year":2020,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Environment and Protected Areas; Trent University; Environment and Climate Change Canada","funders":"","keywords":"Sampling (signal processing); Statistics; Boreal; Sample size determination; Environmental science; Abundance (ecology); Population; Range (aeronautics); Distance sampling; Sampling bias; Ecology; Mathematics; Biology; Computer science","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1980982,0.0008732553,0.0007069767,0.001310661,0.0009819537,0.0012889,0.002001912,0.001121621,0.0005610987],"category_scores_gemma":[0.3468468,0.0007320285,0.001260011,0.00108327,0.001394316,0.001497821,0.001607534,0.0008668487,0.0001880427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00128238,"about_ca_system_score_gemma":0.001756248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003931979,"about_ca_topic_score_gemma":0.00631622,"domain_scores_codex":[0.7782377,0.1978108,0.006392749,0.00631738,0.01038845,0.000852885],"domain_scores_gemma":[0.5163149,0.3880857,0.04430365,0.02514357,0.02499101,0.001161127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002811061,0.0004690886,0.7552088,0.0008696926,0.002811768,0.0001960786,0.00217624,0.0665988,0.01484633,0.004599,0.001575124,0.147838],"study_design_scores_gemma":[0.0006185591,0.006825171,0.4063467,0.0005891001,0.001690578,0.0007770157,0.001009696,0.5375742,0.03004799,0.007656553,0.006597664,0.0002666995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6972064,0.0007795339,0.2973734,0.0004795415,0.00008501811,0.0008163144,0.0003616751,0.0004320612,0.002466027],"genre_scores_gemma":[0.8547886,0.00008945078,0.1437764,0.0002385846,0.0000289863,0.0006690367,0.0002138718,0.00006599097,0.0001291535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1980982,"threshold_uncertainty_score":0.9888877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08234876012183961,"score_gpt":0.3892721978667554,"score_spread":0.3069234377449158,"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."}}