{"id":"W4285595550","doi":"10.1101/2022.07.14.500091","title":"A statistical censoring approach accounts for hook competition in abundance indices from longline surveys","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of British Columbia; Fisheries and Oceans Canada","funders":"British Columbia Knowledge Development Fund; Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Hook; Competition (biology); Censoring (clinical trials); Abundance (ecology); Econometrics; Relative species abundance; Statistics; Fishery; Computer science; Ecology; Mathematics; Biology; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007857461,0.0004673058,0.0005126301,0.001251725,0.000466285,0.0008209966,0.001351116,0.0006885995,0.001366454],"category_scores_gemma":[0.02136027,0.0002312962,0.0008582,0.001491691,0.0005665899,0.0008500366,0.000918938,0.0007936224,0.0003378219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005244794,"about_ca_system_score_gemma":0.0008389117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005267484,"about_ca_topic_score_gemma":0.00799652,"domain_scores_codex":[0.9977701,0.001225413,0.0001502193,0.0003574209,0.0003985276,0.0000982955],"domain_scores_gemma":[0.9791561,0.01336405,0.002415307,0.00349301,0.001198159,0.0003733516],"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":[0.0007539734,0.0002930229,0.2323941,0.0003983352,0.001184624,0.0005077957,0.0004773453,0.3444574,0.0187771,0.01192096,0.003866506,0.3849688],"study_design_scores_gemma":[0.00003278109,0.0001202475,0.05720362,0.00003984294,0.0001195193,0.0002267954,0.00006065232,0.9285131,0.005071005,0.007003441,0.001542229,0.00006670197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2133074,0.0002435447,0.7835257,0.0001750436,0.00005091583,0.00005631072,0.0004558172,0.001239144,0.0009461688],"genre_scores_gemma":[0.7679954,0.0001478352,0.2288492,0.0001044008,0.0000818238,0.0001185406,0.001185827,0.000220449,0.001296445],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007857461,"threshold_uncertainty_score":0.04155469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01576672366528866,"score_gpt":0.2251355661601767,"score_spread":0.209368842494888,"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."}}