{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001849547,0.00004854115,0.000101247,0.00003156595,0.00007629919,0.000001427383,0.0001129955,0.00004840864,0.0001273205],"category_scores_gemma":[0.0007323124,0.00002869116,0.000009593628,0.0001319585,0.000522061,0.00009498074,0.0001301779,0.00006073631,0.000007011643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007115267,"about_ca_system_score_gemma":0.000002729115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009047587,"about_ca_topic_score_gemma":0.006141767,"domain_scores_codex":[0.9990704,0.0004590057,0.0001431438,0.0001411441,0.00004112168,0.000145166],"domain_scores_gemma":[0.9989265,0.0009199467,0.00005657367,0.00008815913,0.000002552934,0.000006236474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001203153,0.00002932871,0.9909751,0.000006062565,0.000002361393,0.000001236617,0.0004464988,0.001133375,0.0005062237,0.002288739,0.0006322286,0.003966815],"study_design_scores_gemma":[0.0001719447,0.00004145042,0.9675896,0.000009671599,0.000003142289,0.0000014,0.000221716,0.002153525,0.00003363598,0.0294246,0.0003135936,0.00003569293],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9465519,0.00003198518,0.04376226,0.002136745,0.0001868492,0.0001647644,6.924546e-7,0.000006083102,0.007158762],"genre_scores_gemma":[0.9417959,0.0000387233,0.0577321,0.0001225128,0.000008304479,0.00003299952,1.201751e-7,0.00000120806,0.000268121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02713586,"threshold_uncertainty_score":0.342725,"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."}}