{"id":"W1830540712","doi":"10.1109/oceans.2000.882163","title":"Bottom classification in very shallow water","year":2002,"lang":"en","type":"article","venue":"","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Quest University Canada","funders":"Ministère de la Défense Nationale","keywords":"Shore; Echo (communications protocol); Geology; Aliasing; SIGNAL (programming language); Noise (video); Convolution (computer science); Acoustics; Range (aeronautics); Computer science; Sonar; Waves and shallow water; Remote sensing; Ambient noise level; Multivariate statistics; Artificial intelligence; Oceanography; Sound (geography); Filter (signal processing); Computer vision; Engineering; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001718802,0.00005297494,0.00005593307,0.0001063442,0.00003893745,0.0000599445,0.0001388878,0.00004406561,0.03323659],"category_scores_gemma":[0.000008315272,0.00003322992,0.00001473216,0.00009970721,0.00003209379,0.0001569504,0.000006679824,0.0001224313,0.00638629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005531027,"about_ca_system_score_gemma":0.000004965315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001001069,"about_ca_topic_score_gemma":0.003230011,"domain_scores_codex":[0.9992201,0.00003711299,0.0001075817,0.000150832,0.0002084629,0.000275889],"domain_scores_gemma":[0.9997555,0.00004064189,0.000005651475,0.0001207251,0.00001470499,0.00006276111],"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":[0.00001215922,0.00004592428,0.9515067,0.00001324087,0.000005942854,0.00003675094,0.0004849154,0.00443863,0.003315723,0.00002011001,0.002873047,0.03724689],"study_design_scores_gemma":[0.0001381613,0.00002893598,0.2486762,0.000002191487,9.721527e-7,0.000003129319,0.0000552097,0.7474506,0.0007972716,0.0003757648,0.002394928,0.00007657221],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7815263,0.00006169817,0.002357342,0.00179095,0.0001206934,0.0001509472,0.000006601322,0.00005662668,0.2139288],"genre_scores_gemma":[0.9921975,0.00003874168,0.00103366,0.0001322831,0.00003243017,6.834424e-7,0.00003410414,0.000001647978,0.006528913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.743012,"threshold_uncertainty_score":0.9943873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06094367580599205,"score_gpt":0.2447963938233314,"score_spread":0.1838527180173394,"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."}}