{"id":"W2913957547","doi":"10.1002/ece3.4824","title":"Effects of distance on detectability of Arctic waterfowl using double‐observer sampling during helicopter surveys","year":2019,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Environment and Climate Change Canada","keywords":"Distance sampling; Estimator; Statistics; Covariate; Observer (physics); Range (aeronautics); Sampling (signal processing); Abundance estimation; Mathematics; Transect; Computer science; Abundance (ecology); Ecology; Biology; Physics; Computer vision","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":[0.01427872,0.0005690628,0.0005399346,0.0005751182,0.0003188976,0.001075446,0.0006970901,0.0005756801,0.0005637353],"category_scores_gemma":[0.04822782,0.0003874442,0.0009533723,0.0004082972,0.0007683412,0.0009076085,0.0007945093,0.0006956583,0.00008812937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008215847,"about_ca_system_score_gemma":0.0003761063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01748939,"about_ca_topic_score_gemma":0.02179624,"domain_scores_codex":[0.9872111,0.008196134,0.0005359769,0.002780849,0.0009727119,0.0003033487],"domain_scores_gemma":[0.9022726,0.0786961,0.01145098,0.004577287,0.002334072,0.0006689468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009057139,0.00007057963,0.9697272,0.00006049005,0.0006858581,0.00007739793,0.0004457569,0.01368873,0.003485189,0.0001719882,0.00009697455,0.01058411],"study_design_scores_gemma":[0.00001815783,0.0009757656,0.8949131,0.00002717896,0.0002771279,0.0001767715,0.0002682337,0.1001547,0.002508703,0.0002420802,0.0003917014,0.00004636783],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903886,0.0001351618,0.00905385,0.00002734721,0.000009085225,0.00001625515,0.00008399483,0.00002324072,0.0002624442],"genre_scores_gemma":[0.9982152,0.00001835574,0.001583455,0.00001219125,0.000004079976,0.000008422852,0.00007288096,0.000008236037,0.00007727943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01748939,"threshold_uncertainty_score":0.07551402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01167519914220646,"score_gpt":0.2170361540543125,"score_spread":0.205360954912106,"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."}}