{"id":"W3041430445","doi":"10.1111/jfb.14456","title":"Length measurement accuracy of adaptive resolution imaging sonar and a predictive model to assess adult Atlantic salmon (<scp><i>Salmo salar</i></scp>) into two size categories with long‐range data in a river","year":2020,"lang":"en","type":"article","venue":"Journal of Fish Biology","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; University of New Brunswick","funders":"Fisheries and Oceans Canada; Miramichi Salmon Association; Atlantic Canada Opportunities Agency; Emil Aaltosen Säätiö; New Brunswick Innovation Foundation; Fondation Pour La Conservation Du Saumon Atlantique","keywords":"Salmo; Fish measurement; Sonar; Range (aeronautics); Accuracy and precision; Intraclass correlation; Population; Biology; Remote sensing; Artificial intelligence; Statistics; Fishery; Computer science; Fish <Actinopterygii>; Mathematics; Reproducibility; Geology; 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":[0.002846957,0.0005871083,0.000398514,0.0008069021,0.0001644694,0.0007705358,0.000767879,0.0004824983,0.0006276793],"category_scores_gemma":[0.008170757,0.0002649078,0.0006627893,0.0004641415,0.0003049568,0.0008870562,0.0009193242,0.0004769255,0.0004362575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004261562,"about_ca_system_score_gemma":0.0005731343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01033933,"about_ca_topic_score_gemma":0.008073142,"domain_scores_codex":[0.9990597,0.0001812638,0.000102019,0.0003905187,0.0001943238,0.00007210003],"domain_scores_gemma":[0.9971852,0.001239107,0.0003821996,0.000322155,0.0007554928,0.0001159006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001316727,0.0002104408,0.5517406,0.0001566792,0.000364935,0.0001552504,0.0004240627,0.2496037,0.02288445,0.0004272436,0.001252854,0.1714632],"study_design_scores_gemma":[0.00002619476,0.0004078216,0.1911747,0.00004575706,0.0001417411,0.0001001621,0.0002450394,0.799089,0.007775827,0.0003279047,0.0005993075,0.00006651686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.978137,0.0001844603,0.02001237,0.00004954338,0.0000293038,0.00001877824,0.0003957129,0.0002201601,0.0009527712],"genre_scores_gemma":[0.9874921,0.00007475436,0.01062926,0.00002243463,0.000009296647,0.0000301029,0.001306927,0.00003061728,0.0004046134],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01033933,"threshold_uncertainty_score":0.0205583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04262350354546229,"score_gpt":0.2626874007412991,"score_spread":0.2200638971958369,"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."}}