{"id":"W4404941366","doi":"10.1007/978-3-031-75329-9_33","title":"An Adaptive Fast-RCNN Method for Fish Monitoring: From an Artificial Environment to the Ocean","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in information systems and organisation","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Fish <Actinopterygii>; Fishery; Environmental science; Artificial intelligence; Computer science; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002596022,0.0006542204,0.0003538145,0.0004941574,0.0002074821,0.0005671456,0.0007982175,0.000746407,0.002191001],"category_scores_gemma":[0.0004665907,0.0002670845,0.00049326,0.0007699208,0.0002343176,0.0005768019,0.0004228891,0.0005223505,0.001261975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003183065,"about_ca_system_score_gemma":0.0004040458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006286317,"about_ca_topic_score_gemma":0.006551541,"domain_scores_codex":[0.9998502,0.00001684549,0.000007778923,0.00005813359,0.00005478496,0.00001229323],"domain_scores_gemma":[0.999881,0.00003218837,0.00001002663,0.00001722188,0.00005347323,0.000006010434],"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":[0.00006734136,0.00003078975,0.0007023805,0.000182875,0.00004927673,0.0001464006,0.00006811436,0.06290308,0.03835265,0.00256394,0.005167009,0.8897661],"study_design_scores_gemma":[0.000007074611,0.00007504404,0.001957399,0.00004419773,0.00005202146,0.0002288298,0.00003586995,0.9646109,0.01413212,0.002562305,0.01625865,0.00003555101],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01167301,0.003996704,0.9771903,0.0001684772,0.0002561334,0.00003928059,0.00009321026,0.001527832,0.005055165],"genre_scores_gemma":[0.1508575,0.006484269,0.8112506,0.0002238576,0.0002342884,0.00009928716,0.0005005926,0.000316305,0.0300333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006286317,"threshold_uncertainty_score":0.01249945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02962503401509097,"score_gpt":0.2675230080318953,"score_spread":0.2378979740168043,"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."}}