{"id":"W2412210192","doi":"10.1016/j.oceaneng.2016.05.039","title":"Application of acoustic image processing in underwater terrain aided navigation","year":2016,"lang":"en","type":"article","venue":"Ocean Engineering","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Terrain; Artificial intelligence; Underwater; Sonar; Computer vision; Bathymetry; Computer science; Pixel; Feature (linguistics); Noise (video); Interpolation (computer graphics); Remote sensing; Geology; Image (mathematics); Geography","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":[0.0002050947,0.0003706425,0.0002326644,0.0006089828,0.0002034151,0.0004627806,0.0002283971,0.000511524,0.002410796],"category_scores_gemma":[0.000704061,0.0001901807,0.0002086098,0.0007272274,0.0002711244,0.0004383733,0.0003107799,0.0002884475,0.0007217632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001512815,"about_ca_system_score_gemma":0.000394731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001826153,"about_ca_topic_score_gemma":0.002428887,"domain_scores_codex":[0.9998878,0.00001972619,0.00000481761,0.00001634071,0.00006166106,0.000009750523],"domain_scores_gemma":[0.9997496,0.00009892712,0.00001502158,0.00002089113,0.0001078349,0.000007651201],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001619894,0.00006842496,0.001479672,0.0002096633,0.00004479115,0.0001819031,0.0001156058,0.02641266,0.3201677,0.004751321,0.00182424,0.644582],"study_design_scores_gemma":[0.00003047282,0.0001809862,0.004423399,0.00004412712,0.0001044602,0.0005235182,0.0001164702,0.6775273,0.2931073,0.003797819,0.02009338,0.00005077605],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05928762,0.00150611,0.9264885,0.0004099291,0.0002538417,0.00005210304,0.000113773,0.0007351656,0.01115308],"genre_scores_gemma":[0.4443051,0.003227796,0.5374448,0.0002266583,0.000239683,0.00005183067,0.0002007982,0.0001328675,0.01417042],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002410796,"threshold_uncertainty_score":0.008064926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006611878973816972,"score_gpt":0.2049192521123096,"score_spread":0.1983073731384926,"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."}}