{"id":"W2043586387","doi":"10.1139/f06-063","title":"Improving the precision of design-based scallop drag surveys using adaptive allocation methods","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stratified sampling; Scallop; Sampling design; Sampling (signal processing); Stratum; Population; Fishery; Boom; Variance (accounting); Sample size determination; Environmental science; Statistics; Computer science; Engineering; Mathematics; Biology; Environmental engineering; Filter (signal processing)","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":[0.02674803,0.0007505039,0.001065014,0.001997178,0.0005537609,0.0006835858,0.00123077,0.0007094909,0.000766093],"category_scores_gemma":[0.07342518,0.0008407607,0.0006415246,0.001458476,0.0007785744,0.000964799,0.001524848,0.0007968344,0.0003358201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008811056,"about_ca_system_score_gemma":0.001251623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002422382,"about_ca_topic_score_gemma":0.004194668,"domain_scores_codex":[0.9714606,0.02165762,0.001479696,0.002383247,0.002657283,0.0003615592],"domain_scores_gemma":[0.9359865,0.03997959,0.006471089,0.01026734,0.006929732,0.0003657182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0011569,0.0004256874,0.09400533,0.0004078645,0.0004181224,0.00007507305,0.001393141,0.1544002,0.02715924,0.005861419,0.0009449676,0.7137519],"study_design_scores_gemma":[0.0009530273,0.003240568,0.1329678,0.0001890689,0.000452884,0.0004239296,0.0003164985,0.7977756,0.03690264,0.01728786,0.009250664,0.0002394562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07139453,0.0001346884,0.9273102,0.00005423191,0.00001758506,0.0001832421,0.00003287698,0.0003649514,0.0005077846],"genre_scores_gemma":[0.2843569,0.00009289803,0.7145819,0.00005582444,0.00002426091,0.0004717594,0.00007903621,0.00004735717,0.0002899634],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02674803,"threshold_uncertainty_score":0.1414588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1693764944447455,"score_gpt":0.354224719654368,"score_spread":0.1848482252096225,"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."}}