{"id":"W1974366701","doi":"10.1890/09-0309.1","title":"An empirical probability model of detecting species at low densities","year":2010,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Marine Ecology and Invasive Species","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Park Service; National Oceanic and Atmospheric Administration","keywords":"Quadrat; Sampling (signal processing); Distance sampling; Statistical power; Ecology; Statistics; Sensitivity (control systems); Computer science; Abundance (ecology); Mathematics; Biology; Filter (signal processing); 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":[0.01186374,0.002051842,0.00253065,0.003429834,0.0009059843,0.003927725,0.00593998,0.004045443,0.00759934],"category_scores_gemma":[0.05610183,0.001549659,0.002098174,0.002376107,0.004196595,0.007605217,0.001460574,0.003888726,0.002834051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00209385,"about_ca_system_score_gemma":0.001069874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01559339,"about_ca_topic_score_gemma":0.005723429,"domain_scores_codex":[0.995715,0.001601905,0.0002289285,0.001498484,0.0005218113,0.0004338411],"domain_scores_gemma":[0.9361212,0.05378386,0.003873728,0.002407273,0.003165677,0.0006482636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004893052,0.0003761125,0.04604925,0.0005741328,0.0003721751,0.001008978,0.001473942,0.7674481,0.003916514,0.1254453,0.004966311,0.04787992],"study_design_scores_gemma":[0.00004680076,0.00009890369,0.005200625,0.00006597755,0.00007907581,0.0004730684,0.00009555835,0.9692222,0.000300148,0.02316971,0.001188987,0.00005893999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1731201,0.0009993586,0.809966,0.00279086,0.0001633092,0.0004655896,0.001698778,0.001246765,0.009549242],"genre_scores_gemma":[0.8657393,0.001766832,0.1005617,0.0007702846,0.0003270202,0.001216084,0.002102725,0.0002438705,0.02727207],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01559339,"threshold_uncertainty_score":0.06274217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.029510261578039,"score_gpt":0.2591629022278117,"score_spread":0.2296526406497727,"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."}}