{"id":"W2104302961","doi":"10.1109/icassp.2008.4518138","title":"Characterization of marine noise using Beaulieu series","year":2008,"lang":"en","type":"article","venue":"","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Series (stratigraphy); Convolution (computer science); Computer science; Noise (video); Sonar; Probability density function; Algorithm; Artificial intelligence; Mathematics; Statistics; Geology; Artificial neural network","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":[0.001322993,0.0006866866,0.0005582075,0.002166505,0.0005505085,0.001114612,0.001157459,0.0008384027,0.001041777],"category_scores_gemma":[0.007157727,0.0002915885,0.0006714592,0.0006937773,0.001119341,0.002070011,0.0006743704,0.001430882,0.0005253718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006290633,"about_ca_system_score_gemma":0.0005154217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002466609,"about_ca_topic_score_gemma":0.001506035,"domain_scores_codex":[0.9994191,0.0001276941,0.00002089098,0.00008979082,0.0002976055,0.00004494268],"domain_scores_gemma":[0.99803,0.001185102,0.0002590233,0.0001484062,0.0002970014,0.00008046207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001042313,0.00004560423,0.003399815,0.00009985296,0.00003996829,0.0003437544,0.0002710249,0.6318662,0.02522284,0.2434874,0.001110624,0.09400879],"study_design_scores_gemma":[0.000001782244,0.00001475249,0.0004213788,0.000007828773,0.000002921674,0.0001055343,0.00001328018,0.9754285,0.002654165,0.02032793,0.001002528,0.00001940042],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01666632,0.0001347488,0.9812585,0.00005297744,0.00002673901,0.00001106472,0.00002065923,0.0001345486,0.001694403],"genre_scores_gemma":[0.6256627,0.001127916,0.3665881,0.0001321356,0.0002661046,0.000086543,0.0003199892,0.0002636728,0.005552862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002466609,"threshold_uncertainty_score":0.006996751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03707161388717396,"score_gpt":0.2370958766226783,"score_spread":0.2000242627355043,"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."}}