{"id":"W2516061122","doi":"","title":"Efficient estimation of range-dependent seabed properties from large data volumes of a towed source and receiver-array system","year":2016,"lang":"en","type":"article","venue":"Canadian acoustics","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":"Computer science; Sonar; Seabed; Inversion (geology); Particle filter; Computation; Computational science; Algorithm; Filter (signal processing); Geology; Computer vision; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004420049,0.0001218281,0.0002277226,0.0001758433,0.0001047081,0.00004127654,0.0004348561,0.00008931076,0.0002631017],"category_scores_gemma":[0.0002716755,0.00008416655,0.00001728511,0.0001232129,0.0001759075,0.00007910051,0.00003903385,0.00007870815,0.00003487801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004916212,"about_ca_system_score_gemma":0.000454682,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1220259,"about_ca_topic_score_gemma":0.05560625,"domain_scores_codex":[0.998556,0.00009097531,0.0002820965,0.000290081,0.0004008878,0.0003799541],"domain_scores_gemma":[0.9987488,0.0001784491,0.00009773774,0.0004940538,0.0001383564,0.0003425811],"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":[0.0003189805,0.00009236272,0.2144665,0.00155555,0.0003384273,0.00009089139,0.003978778,0.5039481,0.1601223,0.0000150982,0.003924006,0.111149],"study_design_scores_gemma":[0.0004832383,0.00006042646,0.03726459,0.000208813,0.00005641717,0.000005505189,0.0007414072,0.9600254,0.0008805658,0.00001267335,0.0001248954,0.0001360136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8286355,0.0004998149,0.1632642,0.000126775,0.0001685781,0.0003186436,0.006664379,0.00002320741,0.0002989474],"genre_scores_gemma":[0.9969119,0.00002950604,0.002475264,0.000016657,0.00004158577,8.578145e-7,0.0001383933,0.000007905847,0.000377971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4560773,"threshold_uncertainty_score":0.9616265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02727993985825682,"score_gpt":0.2168368038379047,"score_spread":0.1895568639796479,"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."}}