{"id":"W4205761787","doi":"10.3389/fmars.2021.823173","title":"Automated Detection, Classification and Counting of Fish in Fish Passages With Deep Learning","year":2022,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":122,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Research Chairs","keywords":"Convolutional neural network; Sonar; Computer science; Artificial intelligence; Adaptation (eye); Fish <Actinopterygii>; Citizen science; Fishery; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007089116,0.0009506122,0.0004733327,0.0008234284,0.0002915212,0.0005579061,0.001382781,0.0008061643,0.0009077499],"category_scores_gemma":[0.001340521,0.0004515327,0.0007604032,0.0005013085,0.0004079558,0.0009877081,0.0009879576,0.001043675,0.0006347637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001189511,"about_ca_system_score_gemma":0.001087109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02314991,"about_ca_topic_score_gemma":0.03699706,"domain_scores_codex":[0.9995909,0.00004557392,0.00002290272,0.000182853,0.0000770977,0.00008070528],"domain_scores_gemma":[0.9993856,0.0002147361,0.00009172651,0.00009420393,0.0001677697,0.00004595185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003778021,0.0003671963,0.02686012,0.0001545172,0.0002069119,0.0001858667,0.0001619861,0.295434,0.04206132,0.001658263,0.007804161,0.6247278],"study_design_scores_gemma":[0.000006867447,0.0000424378,0.002614122,0.0000101177,0.00001198729,0.00001826515,0.00001672233,0.9904855,0.005618603,0.0006702445,0.0004957956,0.000009303963],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3162456,0.0008786842,0.6667249,0.0005412456,0.0002295749,0.0001273461,0.001552767,0.009903144,0.003796681],"genre_scores_gemma":[0.7384567,0.0002199846,0.2537193,0.0003025224,0.00004556476,0.0001054849,0.002496634,0.0001334723,0.00452024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02314991,"threshold_uncertainty_score":0.04603034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004535035949910538,"score_gpt":0.1929162966942858,"score_spread":0.1883812607443753,"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."}}