{"id":"W1967533322","doi":"10.1139/cjfas-2014-0181","title":"The spatial distribution of salmon and steelhead redds and optimal sampling design","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Marine Fisheries Service; Bonneville Power Administration","keywords":"Stratified sampling; Sampling (signal processing); Sampling design; Statistics; Simple random sample; Population; Environmental science; Spatial distribution; Census; Sample size determination; Hydrology (agriculture); Mathematics; Computer science; Geology","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":[0.01265564,0.0004098924,0.0006086041,0.0007374574,0.0003817567,0.0005837443,0.0009996393,0.0005073659,0.001055078],"category_scores_gemma":[0.02862644,0.0005420108,0.0007677894,0.0008638197,0.001106147,0.0006137269,0.0009115448,0.0003547164,0.000258047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001157926,"about_ca_system_score_gemma":0.001571591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0103139,"about_ca_topic_score_gemma":0.02030743,"domain_scores_codex":[0.9813995,0.01457719,0.0007029581,0.001937061,0.0009964354,0.0003869234],"domain_scores_gemma":[0.98884,0.005576714,0.001619274,0.002260976,0.001463559,0.000239316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003182236,0.0004841586,0.3862754,0.0005946479,0.0007596485,0.000288625,0.001287367,0.3453139,0.0215153,0.01655465,0.001583094,0.2221609],"study_design_scores_gemma":[0.001082265,0.006036393,0.4222397,0.0001826631,0.0006632878,0.000422179,0.001083998,0.5069177,0.02147245,0.0300038,0.009720266,0.0001752518],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5526237,0.0002897176,0.4422225,0.0001513369,0.00002758025,0.001811581,0.0006144354,0.0003083534,0.0019508],"genre_scores_gemma":[0.8053371,0.0001061514,0.1919855,0.00006339491,0.000005614798,0.00168601,0.0004485854,0.00002480214,0.0003429272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01265564,"threshold_uncertainty_score":0.06693023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02068295339112966,"score_gpt":0.2135730467487226,"score_spread":0.1928900933575929,"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."}}