{"id":"W1851368146","doi":"10.1139/cjfas-2013-0508","title":"Spatial semiparametric models improve estimates of species abundance and distribution","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":152,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Northwest Fisheries Science Center; National Marine Fisheries Service","keywords":"Abundance (ecology); Estimator; Population; Econometrics; Statistics; Covariate; Inference; Sebastes; Ecology; Mathematics; Biology; Computer science; Fishery; Fish <Actinopterygii>","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01169514,0.001046728,0.001149087,0.001606578,0.0003342846,0.002191948,0.001317381,0.001295812,0.001962882],"category_scores_gemma":[0.06285659,0.0008365975,0.001884519,0.001632495,0.0009689203,0.003017912,0.00235922,0.001560638,0.0006320366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007461997,"about_ca_system_score_gemma":0.0008408317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00299487,"about_ca_topic_score_gemma":0.004371971,"domain_scores_codex":[0.9930027,0.005360506,0.0002848867,0.0007656597,0.0004839566,0.0001022843],"domain_scores_gemma":[0.9326161,0.05555272,0.004461216,0.005548632,0.001619676,0.0002016655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002648428,0.000134015,0.07599204,0.0009913887,0.002868978,0.0002487386,0.0007849837,0.6063606,0.002797291,0.09203903,0.003608834,0.2139092],"study_design_scores_gemma":[0.00005299128,0.0001713728,0.01609561,0.0001317268,0.000572712,0.0002016513,0.0001331789,0.799279,0.001292218,0.1757851,0.006191345,0.00009313573],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03330009,0.001205426,0.9626758,0.0004673754,0.00003986472,0.00003960738,0.0004314756,0.0005941765,0.001246281],"genre_scores_gemma":[0.6903571,0.001462568,0.3045901,0.0004955464,0.0001473999,0.0002127185,0.0008873633,0.0002789471,0.001568167],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01169514,"threshold_uncertainty_score":0.06185061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.013100491771675,"score_gpt":0.1925546849086313,"score_spread":0.1794541931369563,"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."}}