{"id":"W2345417231","doi":"10.1121/1.4950283","title":"Geoacoustic inference and the search for ground truth","year":2016,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Seabed; Ground truth; Inference; Computer science; Geology; Attenuation; Range (aeronautics); Reflection (computer programming); Acoustics; Artificial intelligence; Oceanography; 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":[0.01003286,0.001059561,0.00184498,0.005292867,0.001072062,0.004609981,0.004015445,0.003529942,0.004243838],"category_scores_gemma":[0.06475975,0.001135052,0.0009439308,0.002921428,0.005253982,0.008410755,0.002845678,0.003231389,0.001837679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00162308,"about_ca_system_score_gemma":0.002726515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004786398,"about_ca_topic_score_gemma":0.003316886,"domain_scores_codex":[0.9942826,0.002131274,0.0003854575,0.001730735,0.001167482,0.0003024478],"domain_scores_gemma":[0.9309676,0.0542969,0.003271027,0.006787235,0.004240467,0.000436828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005322976,0.0002145687,0.0195313,0.0009959748,0.0003047865,0.0007680763,0.0007447352,0.3424162,0.003089489,0.2454895,0.01529976,0.3706133],"study_design_scores_gemma":[0.00005285364,0.00006412134,0.001773507,0.0003134093,0.00004481376,0.0001651392,0.000293761,0.4553007,0.002490921,0.5336877,0.005757947,0.00005511067],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02321696,0.001717456,0.9659206,0.002904631,0.000194894,0.00004021068,0.0009169215,0.001112537,0.003975802],"genre_scores_gemma":[0.6601034,0.00158313,0.3303282,0.001228771,0.0005029808,0.0001262307,0.004427194,0.0002919654,0.001407973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01003286,"threshold_uncertainty_score":0.05305946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02777956928492741,"score_gpt":0.2757060474348583,"score_spread":0.2479264781499309,"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."}}