{"id":"W2312901664","doi":"10.1111/2041-210x.12570","title":"Estimating abundance in the presence of species uncertainty","year":2016,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Churchill Northern Studies Centre","funders":"U.S. Geological Survey; Churchill Northern Studies Centre; Earthwatch Institute","keywords":"Abundance (ecology); Covariate; Abundance estimation; Mixture model; Markov chain Monte Carlo; Bayesian probability; Inference; Bayesian inference; Statistics; Computer science; Ecology; Mathematics; Biology; Artificial intelligence","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":[0.004916155,0.0005029339,0.0008128185,0.001160878,0.0003263465,0.001012731,0.001128905,0.0007267656,0.001439796],"category_scores_gemma":[0.02265003,0.0005694013,0.001087647,0.0008994906,0.0006195744,0.001810143,0.001285881,0.0008172277,0.0002690366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009200983,"about_ca_system_score_gemma":0.0006575001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008211907,"about_ca_topic_score_gemma":0.007525136,"domain_scores_codex":[0.9974087,0.001089843,0.0001593277,0.0006915776,0.0005267567,0.0001238515],"domain_scores_gemma":[0.9863484,0.01035691,0.001223275,0.001143125,0.0007906254,0.0001376616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002535668,0.00005379608,0.0991922,0.0002129465,0.0003159598,0.0001847639,0.0003865687,0.7683766,0.005399369,0.01695997,0.001606187,0.1070581],"study_design_scores_gemma":[0.00001064707,0.00003304008,0.01317606,0.00003215633,0.00003189036,0.0001155853,0.0000545934,0.9613862,0.001377649,0.02241971,0.001320447,0.0000420489],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1712404,0.0001898568,0.8263109,0.0001850027,0.0000256282,0.00004354883,0.000504411,0.000393633,0.001106681],"genre_scores_gemma":[0.8624849,0.00009173103,0.1354604,0.00007477407,0.00003296571,0.0000967903,0.0008378832,0.00008199835,0.0008385411],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008211907,"threshold_uncertainty_score":0.02599949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02190738240561931,"score_gpt":0.314937878944935,"score_spread":0.2930304965393157,"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."}}