{"id":"W7106298936","doi":"10.20383/103.01402","title":"Hydrophone Anomaly Detections at Ocean Network Canada’s Cabled Observatories","year":2025,"lang":"","type":"dataset","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hydrophone; Underwater; Anomaly (physics); Geophone; Anomaly detection; Data set; Data quality; Waveform","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005931123,0.00070455,0.0003857954,0.002085751,0.001089788,0.0008126295,0.001042289,0.0006412605,0.002058234],"category_scores_gemma":[0.002889806,0.0002889969,0.0003361606,0.002591459,0.0005797637,0.0005701882,0.001051708,0.0007925988,0.001707665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004975461,"about_ca_system_score_gemma":0.006950125,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8130495,"about_ca_topic_score_gemma":0.9133872,"domain_scores_codex":[0.998069,0.00007446289,0.00006914067,0.0003647281,0.001079642,0.0003431387],"domain_scores_gemma":[0.996528,0.0001758222,0.0002045622,0.0003399261,0.002379257,0.0003724557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00082599,0.000248669,0.5595706,0.0004510114,0.000210314,0.00113559,0.001834105,0.01511697,0.01998552,0.001855493,0.2554017,0.1433641],"study_design_scores_gemma":[0.00009134337,0.00008463162,0.7877657,0.0001603585,0.00005809406,0.0002373324,0.001786848,0.05465062,0.01408054,0.000844747,0.1400896,0.0001502211],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.623162,0.0006896319,0.00905324,0.0008822315,0.0002921,0.0002741881,0.3334847,0.006367341,0.02579461],"genre_scores_gemma":[0.5321278,0.0003527619,0.01648944,0.0001990005,0.00004860901,0.0001335812,0.4375019,0.0005166141,0.0126304],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.1869505,"threshold_uncertainty_score":0.3761029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02648959776103851,"score_gpt":0.272072504319024,"score_spread":0.2455829065579855,"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."}}