{"id":"W2800865825","doi":"10.1139/cjfas-2017-0306","title":"A spatial kernel density method to estimate the diet composition of fish","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Monte Carlo method; Statistics; Spatial correlation; Kernel (algebra); Spatial analysis; Mathematics; Mean squared error; Ratio estimator; Correlation; Bias of an estimator","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":[0.001842597,0.0003375745,0.0004102265,0.001462528,0.0002610255,0.0003925437,0.0008924688,0.0004035333,0.00100055],"category_scores_gemma":[0.007701257,0.0002936709,0.0006808398,0.001150396,0.0002823308,0.0008417226,0.0006729644,0.0005214631,0.000265019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005040113,"about_ca_system_score_gemma":0.0006124029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008037517,"about_ca_topic_score_gemma":0.005688468,"domain_scores_codex":[0.9993987,0.0002805356,0.00003489386,0.0001006118,0.0001595407,0.00002566763],"domain_scores_gemma":[0.9970072,0.001679374,0.0003352877,0.0003814421,0.0005522279,0.00004454054],"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":[0.0001732251,0.0001919601,0.03884365,0.0002181975,0.0006577974,0.0001432274,0.000251541,0.5081771,0.008177958,0.03456673,0.001488183,0.4071104],"study_design_scores_gemma":[0.0000103688,0.00002646181,0.004030404,0.000009972682,0.00002692368,0.00007227909,0.00001474618,0.989795,0.0008528749,0.004197306,0.0009446549,0.00001906409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0197531,0.0001023764,0.9795442,0.00003183907,0.00001072487,0.00002349668,0.00006078462,0.0001646017,0.0003088453],"genre_scores_gemma":[0.3896227,0.0002330955,0.608423,0.0000333483,0.00003597948,0.0001319318,0.0003202516,0.00005569492,0.001144051],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008037517,"threshold_uncertainty_score":0.01598144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06512900328070313,"score_gpt":0.2556078191495244,"score_spread":0.1904788158688213,"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."}}