{"id":"W2769707768","doi":"10.1121/1.5014348","title":"Statistical inference for sediment parameters using ship noise and a horizontal array","year":2017,"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; Geology; Statistical inference; Noise (video); Broadband; Traverse; Acoustics; Statistical model; Bayesian inference; Principle of maximum entropy; Bayesian probability; Marine engineering; Computer science; Oceanography; Geodesy; Statistics; Mathematics; Machine learning; Telecommunications; Artificial intelligence","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":[0.002706099,0.0004931131,0.0003535181,0.0006605039,0.0002754492,0.0008130361,0.0004976317,0.0003764117,0.0003540265],"category_scores_gemma":[0.0154215,0.0004283199,0.0004823899,0.0005053491,0.001046747,0.001505047,0.0008339881,0.0005449667,0.00007367996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005611577,"about_ca_system_score_gemma":0.00077725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006407369,"about_ca_topic_score_gemma":0.007308892,"domain_scores_codex":[0.9991857,0.0003383259,0.00004319154,0.0001807754,0.0002036382,0.00004820056],"domain_scores_gemma":[0.9935576,0.005409841,0.0005123717,0.0002696952,0.0002013671,0.00004915015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001107491,0.00004975536,0.02396394,0.00003851509,0.0001309089,0.00008611595,0.0001005513,0.9256092,0.005564894,0.009251211,0.00008793519,0.0350063],"study_design_scores_gemma":[0.000007853727,0.00003247663,0.006115897,0.000005982411,0.00001516177,0.00001785127,0.00002203819,0.9854639,0.001641352,0.006572227,0.00009266001,0.00001261858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3298568,0.00007869595,0.6688146,0.00009645359,0.00001091558,0.00001665633,0.0001022437,0.00009850747,0.0009251437],"genre_scores_gemma":[0.9391219,0.00009109487,0.06000907,0.00003651091,0.00002661164,0.00002302497,0.0001899906,0.0000299221,0.0004717225],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006407369,"threshold_uncertainty_score":0.01431137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04841338866501457,"score_gpt":0.3116497090878529,"score_spread":0.2632363204228383,"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."}}