{"id":"W2001918496","doi":"10.1109/oceans.2014.7003291","title":"Onboard real-time SAS processing — Sea trials and results","year":2014,"lang":"en","type":"article","venue":"","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kraken Sonar (Canada)","funders":"Indian National Science Academy","keywords":"Workflow; Sea trial; Computer science; Beamforming; Sonar; Bandwidth (computing); Data processing; Real-time computing; Synthetic aperture sonar; Systems engineering; Telecommunications; Engineering; Database; Marine engineering; Artificial intelligence","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":[0.003042169,0.001002521,0.0007271394,0.0009157935,0.000818761,0.001586881,0.0008036438,0.0005979454,0.008793149],"category_scores_gemma":[0.002830159,0.0002215763,0.0005156319,0.00107136,0.0008629297,0.00142635,0.0008683913,0.0007145124,0.003574365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000583759,"about_ca_system_score_gemma":0.0008141897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003928854,"about_ca_topic_score_gemma":0.00508589,"domain_scores_codex":[0.9985232,0.000333971,0.00007739659,0.000140882,0.0007344875,0.00019016],"domain_scores_gemma":[0.99751,0.0004043395,0.0001064866,0.0003915119,0.001306385,0.0002812944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004248119,0.003110199,0.03287494,0.0007033353,0.000466219,0.0008036494,0.001546147,0.04615372,0.2017367,0.005794872,0.0447986,0.6577635],"study_design_scores_gemma":[0.0009204622,0.01505127,0.06512997,0.0001350332,0.000334233,0.0006051078,0.002520421,0.07859594,0.5942661,0.003327184,0.238784,0.0003302972],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.715173,0.0008117317,0.159813,0.001049487,0.0005027689,0.002023176,0.01288703,0.008962628,0.09877726],"genre_scores_gemma":[0.8462583,0.0005075029,0.1012547,0.0002666199,0.0001167835,0.0008498633,0.01446684,0.00210557,0.03417385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008793149,"threshold_uncertainty_score":0.02941602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0662595812935456,"score_gpt":0.3088550029057889,"score_spread":0.2425954216122433,"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."}}