{"id":"W4250184449","doi":"10.1121/1.4806195","title":"Spectral probability density as a tool for marine ambient noise analysis","year":2013,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Seismic Waves and Analysis","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ocean Networks Canada Society; University of Victoria","funders":"","keywords":"Ambient noise level; Acoustics; Noise (video); Spectral density; Probability density function; Environmental science; Computer science; Underwater; Frequency domain; Outlier; Probability distribution; Remote sensing; Geology; Telecommunications; Statistics; Physics; Mathematics; Oceanography; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006704074,0.0001093707,0.0003833101,0.0000203894,0.0001890978,0.00003064148,0.0004868988,0.00003949575,0.001524913],"category_scores_gemma":[0.0002154646,0.00004998572,0.001097432,0.0005419931,0.0004143697,0.00008719399,0.00004987985,0.0002112324,0.00001376771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001387181,"about_ca_system_score_gemma":0.00005762411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00409522,"about_ca_topic_score_gemma":0.00002763619,"domain_scores_codex":[0.9987204,0.0001164367,0.00041446,0.0001031729,0.0004070636,0.0002384834],"domain_scores_gemma":[0.9984751,0.0005443796,0.0003978855,0.0002791753,0.0002059429,0.00009752839],"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":[0.0006549633,0.0005162944,0.3985568,0.000129554,0.007262601,0.000001987955,0.001237381,0.462296,0.004731307,0.00001703713,0.03928775,0.08530828],"study_design_scores_gemma":[0.0001820551,0.000248698,0.4359166,0.000006389208,0.002020226,0.00001185459,0.000620398,0.5516575,0.0001747821,0.008762663,0.0003089269,0.00008987245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9409832,0.0000489766,0.05121891,0.007336547,0.00007829758,0.000204144,0.00002099865,0.000004464097,0.0001044701],"genre_scores_gemma":[0.9697086,0.0001156675,0.02822638,0.001664801,0.0001275195,3.63015e-7,0.000003256411,0.000002281341,0.0001510937],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08936152,"threshold_uncertainty_score":0.9993878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008994980193112479,"score_gpt":0.2141871185826639,"score_spread":0.2051921383895514,"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."}}