{"id":"W2016297885","doi":"10.1121/1.428918","title":"Determining a geoacoustic model from shallow-water transmission loss data using parameter linkage and a hybrid inversion algorithm","year":2000,"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":"","funders":"","keywords":"Geology; Acoustics; Speed of sound; Inversion (geology); Simulated annealing; Waves and shallow water; Sound transmission class; Transmission loss; Shear (geology); Shear waves; Algorithm; Computer science; Seismology; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001003512,0.0006820289,0.0006064912,0.0007162815,0.0004435773,0.0009746241,0.001025253,0.0009626335,0.001118648],"category_scores_gemma":[0.003366384,0.000670601,0.0006863258,0.0005484758,0.0004455301,0.001127577,0.0005460056,0.00081436,0.0005379746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006276748,"about_ca_system_score_gemma":0.001787109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009051149,"about_ca_topic_score_gemma":0.006570368,"domain_scores_codex":[0.9997413,0.00006395365,0.00001789264,0.00007500615,0.00006790272,0.00003396022],"domain_scores_gemma":[0.9991363,0.0004799314,0.00006524061,0.00006518339,0.000233453,0.00001992301],"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":[0.0000701835,0.00006025626,0.002097887,0.00003587974,0.00003983398,0.00003656139,0.0001146043,0.9235731,0.01006352,0.0023998,0.0001824252,0.06132603],"study_design_scores_gemma":[0.000006412365,0.0000105649,0.0001843518,0.000002015747,0.00000427328,0.000008380378,0.000008057217,0.998,0.001196046,0.0004630237,0.0001108875,0.000006060016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05128297,0.00001406261,0.9474986,0.00002513836,0.000003799366,0.00004450842,0.00004253699,0.0006661218,0.0004221235],"genre_scores_gemma":[0.4847017,0.0000427064,0.5133539,0.00003554399,0.00000912316,0.0003657271,0.0002834691,0.000156695,0.001051142],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009051149,"threshold_uncertainty_score":0.01799691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04103477909639162,"score_gpt":0.2656569073548286,"score_spread":0.224622128258437,"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."}}