{"id":"W3010293984","doi":"10.1109/globecom38437.2019.9014020","title":"Passive Underwater Event and Object Detection Based on Time Difference of Arrival","year":2019,"lang":"en","type":"article","venue":"","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Underwater; Computer science; Arrival time; Event (particle physics); Time of arrival; Object detection; Object (grammar); Real-time computing; Artificial intelligence; Telecommunications; Geology; Physics; Engineering; Pattern recognition (psychology)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004092445,0.0000693126,0.0001007335,0.00004210144,0.00001447855,0.00001299888,0.00006655554,0.00003583729,0.00009554752],"category_scores_gemma":[4.337274e-7,0.000053272,0.00002461218,0.00003840609,0.000009249006,0.00002624119,0.00001586528,0.00005217219,0.00008308262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002271161,"about_ca_system_score_gemma":0.0000034916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002144578,"about_ca_topic_score_gemma":0.000008612883,"domain_scores_codex":[0.9996209,0.00002519754,0.0001206433,0.00007548355,0.0000822335,0.00007559727],"domain_scores_gemma":[0.9997001,0.00003794246,0.00001918889,0.0002028239,0.00001555348,0.0000243827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002779502,0.0000325236,0.002737973,0.0001244171,0.00004257911,3.383989e-7,0.0002890365,0.01777213,0.9582253,0.00006230207,0.00001659085,0.02066904],"study_design_scores_gemma":[0.0004008063,0.0001341727,0.01418224,0.00005302713,0.000005671801,0.000001596414,0.00006120812,0.4941294,0.4903762,0.0001516734,0.000377387,0.0001266403],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9523724,0.0000143791,0.04284613,0.00004947194,0.0000347197,0.0001432447,0.000001766987,0.00008836621,0.004449563],"genre_scores_gemma":[0.9992111,0.000004244203,0.0001407798,0.00002404624,0.000007486023,0.000007013918,0.000001898985,0.000009981902,0.0005934267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4763573,"threshold_uncertainty_score":0.2172369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005835920841430184,"score_gpt":0.1841574439890677,"score_spread":0.1783215231476376,"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."}}