{"id":"W4386934647","doi":"10.3389/fmars.2023.1200408","title":"Wild salmon enumeration and monitoring using deep learning empowered detection and tracking","year":2023,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Skeena Fisheries Commission; Douglas College; Pacific Salmon Foundation; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fishery; Fish migration; Abundance (ecology); Deep learning; Computer science; Artificial intelligence; Object detection; Metadata; Environmental resource management; Geography; Fish <Actinopterygii>; Environmental science; Biology; Pattern recognition (psychology); World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006108838,0.00006921682,0.00007827779,0.0001524157,0.0005630258,0.00005340644,0.00007811545,0.0000261745,0.00001042925],"category_scores_gemma":[0.0001311365,0.00007348338,0.000006277099,0.0006772976,0.0003581026,0.0005677121,0.0005172238,0.0001060699,0.00000385032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001057899,"about_ca_system_score_gemma":0.000002525718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007786405,"about_ca_topic_score_gemma":0.0001228571,"domain_scores_codex":[0.999213,0.00002559239,0.00009503263,0.0002918738,0.000145003,0.0002294659],"domain_scores_gemma":[0.9998504,0.00001735061,0.00003908472,0.00005721471,0.000004254125,0.00003167491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004521959,0.0000032375,0.9268299,0.000004153482,0.000001609184,0.000003219103,0.0004600532,0.002422645,0.004381348,0.000001997963,0.00001962096,0.06586769],"study_design_scores_gemma":[0.0001100779,0.0000238815,0.9491113,0.000005999917,0.000004193162,0.00000183336,0.0008497144,0.04856327,0.0006777133,0.0004546768,0.0001197541,0.00007752742],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954628,0.00001094162,0.002845715,0.00006159642,0.0004843963,0.00009850434,4.935983e-8,0.00004344195,0.0009924911],"genre_scores_gemma":[0.9911762,0.0001461648,0.008487418,0.00001591368,0.0000174434,0.000006301854,2.92299e-7,0.000004130336,0.0001461108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06579016,"threshold_uncertainty_score":0.4330396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009826739671182283,"score_gpt":0.2330433951828108,"score_spread":0.2232166555116286,"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."}}