{"id":"W2623835778","doi":"10.1121/1.4989339","title":"Computing navigation corrections for co-registration of repeat-pass synthetic aperture sonar images","year":2017,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Image warping; Residual; Interferometry; Synthetic aperture sonar; Coherence (philosophical gambling strategy); Affine transformation; Computer vision; Artificial intelligence; Algorithm; Synthetic aperture radar; Mathematics; Optics; Physics","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":[0.0005295123,0.0008185218,0.0005109078,0.000723084,0.0003483435,0.0006729054,0.0005844037,0.0005555922,0.001976821],"category_scores_gemma":[0.00388201,0.0004789887,0.00056102,0.0008142948,0.0002297808,0.0006271854,0.0006018566,0.0007944063,0.00155475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004459794,"about_ca_system_score_gemma":0.001227217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005669368,"about_ca_topic_score_gemma":0.01030477,"domain_scores_codex":[0.999485,0.00006089874,0.00003342266,0.0001314047,0.000234469,0.00005464298],"domain_scores_gemma":[0.9993182,0.0001405815,0.0001246194,0.0001270313,0.0002697796,0.00001974828],"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":[0.000380898,0.0001632571,0.00654463,0.0001480463,0.0001585878,0.0001948169,0.0005223604,0.2692703,0.1629797,0.004969793,0.00355638,0.5511113],"study_design_scores_gemma":[0.00002974219,0.0002537714,0.01353138,0.00002000227,0.00008536901,0.0002237068,0.0002018453,0.844498,0.1261553,0.001894329,0.01302493,0.000081618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1466204,0.0001315078,0.8481776,0.0001033234,0.0001152669,0.00006695356,0.0001692486,0.002759065,0.001856606],"genre_scores_gemma":[0.291647,0.0001493627,0.7026387,0.0000400262,0.00002564087,0.0001020327,0.0006423736,0.000683795,0.004071021],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005669368,"threshold_uncertainty_score":0.01127273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01038323717159009,"score_gpt":0.2675000953770082,"score_spread":0.2571168582054181,"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."}}