{"id":"W2903368450","doi":"10.3390/geosciences8120455","title":"A Multispectral Bayesian Classification Method for Increased Acoustic Discrimination of Seabed Sediments Using Multi-Frequency Multibeam Backscatter Data","year":2018,"lang":"en","type":"article","venue":"Geosciences","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Deltares","keywords":"Seabed; Backscatter (email); Geology; Remote sensing; Multispectral image; Ground truth; Acoustics; Computer science; Oceanography; Artificial intelligence; Telecommunications","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":[0.001120059,0.0005337979,0.000481796,0.001999576,0.0003867583,0.0007142799,0.0006594019,0.0006301629,0.001658745],"category_scores_gemma":[0.002262769,0.0003737014,0.0005292327,0.0009273483,0.0002649495,0.0007002862,0.0008721211,0.0006492114,0.000872136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003899487,"about_ca_system_score_gemma":0.0007424902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009352611,"about_ca_topic_score_gemma":0.01806869,"domain_scores_codex":[0.9993415,0.0001202203,0.00004883899,0.0001414905,0.0002798662,0.00006808212],"domain_scores_gemma":[0.9992154,0.0001737526,0.00009360458,0.00009383338,0.0003811435,0.00004220401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002303612,0.0003324038,0.01448517,0.0001289161,0.0001156823,0.00008794163,0.0001941631,0.07023555,0.06085854,0.003712167,0.003574047,0.8460451],"study_design_scores_gemma":[0.00002749086,0.0000619311,0.02194031,0.00003355285,0.0000527132,0.0001341493,0.00006760232,0.9601616,0.01131095,0.00251936,0.003623758,0.00006662442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04210666,0.0001371595,0.9547976,0.0001140715,0.00002891495,0.0000452783,0.0002360259,0.001044844,0.001489355],"genre_scores_gemma":[0.2829663,0.0001652584,0.7126769,0.0001443844,0.00005579393,0.0001292199,0.001036257,0.0001618076,0.002664205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009352611,"threshold_uncertainty_score":0.01859635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1408932220321895,"score_gpt":0.3824508166522685,"score_spread":0.2415575946200789,"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."}}