{"id":"W2042364663","doi":"10.1121/1.4785600","title":"Differentiating fish targets from non-fish targets using an imaging sonar and a conventional sonar: Dual frequency identification sonar (DIDSON) versus split-beam sonar","year":2005,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pacific Salmon Commission","funders":"","keywords":"Sonar; Underwater; Fish <Actinopterygii>; Target strength; Environmental science; Synthetic aperture sonar; Acoustics; Remote sensing; Geology; Marine engineering; Computer science; Fishery; Oceanography; Engineering; Biology; Physics","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":[0.0006403844,0.000398799,0.0005494781,0.0009888682,0.0002301152,0.0006827713,0.0003864434,0.0005743587,0.001568122],"category_scores_gemma":[0.001220418,0.0003937908,0.0002166556,0.0006655288,0.0004024697,0.001075958,0.0006414705,0.0003115429,0.0006399201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000265335,"about_ca_system_score_gemma":0.0004363284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002027323,"about_ca_topic_score_gemma":0.009864465,"domain_scores_codex":[0.9996221,0.00003212132,0.00002782131,0.000120296,0.0001595817,0.00003818305],"domain_scores_gemma":[0.999511,0.0001479385,0.0000681769,0.00004012005,0.0001885779,0.00004414717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001060793,0.0001315708,0.08852287,0.0004819851,0.00007987205,0.0002175919,0.0005534552,0.0007586076,0.5682736,0.0004456803,0.0008822564,0.3385918],"study_design_scores_gemma":[0.0002097963,0.001743435,0.6851494,0.0001668016,0.0003696436,0.003713258,0.002124036,0.03226742,0.2625257,0.001139659,0.01043011,0.00016066],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8934305,0.0013355,0.09908038,0.0002480201,0.00009441275,0.0000863391,0.000359409,0.0002832076,0.005082187],"genre_scores_gemma":[0.8009934,0.0009161193,0.1942446,0.0002280262,0.00004690519,0.000097681,0.0005153373,0.0000566048,0.002901305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002027323,"threshold_uncertainty_score":0.005245864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01234025936579626,"score_gpt":0.2405256542927587,"score_spread":0.2281853949269625,"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."}}