{"id":"W4232658957","doi":"10.1109/joe.2017.2662958","title":"Corrections to “A Bayesian Method for Localization by Multistatic Active Sonar” [IEEE J. Ocean. Eng., vol. 42, no. 1, pp. 135–142, Jan. 2017, DOI: 10.1109/JOE.2016.2540744]","year":2017,"lang":"en","type":"article","venue":"IEEE Journal of Oceanic Engineering","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Sonar; Marine mammals and sonar; Computer science; Bayesian probability; Remote sensing; Artificial intelligence; Marine engineering; Engineering; Geology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01030204,0.002583316,0.001233946,0.002861058,0.002553456,0.003325458,0.004721465,0.006748968,0.02103096],"category_scores_gemma":[0.06905933,0.001561431,0.002659635,0.002535286,0.003468567,0.004779135,0.002990928,0.01603452,0.01848965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003486029,"about_ca_system_score_gemma":0.006019486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02376569,"about_ca_topic_score_gemma":0.03427346,"domain_scores_codex":[0.98422,0.004674391,0.001446426,0.001495152,0.007776184,0.0003878844],"domain_scores_gemma":[0.95879,0.009926764,0.00171013,0.003056951,0.02556424,0.000951916],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000556059,0.00003316026,0.0001972326,0.000235116,0.00005975414,0.0001422233,0.0001058958,0.001238978,0.0008591856,0.0187581,0.9453203,0.03299446],"study_design_scores_gemma":[0.00004354319,0.00003647863,0.0005979444,0.0002129626,0.00005958339,0.0003710781,0.00006616914,0.006814514,0.002664389,0.02393009,0.9650427,0.0001605879],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"methods","genre_scores_codex":[0.0008118792,0.01126004,0.2566719,0.1997016,0.5105365,0.0001423142,0.002054534,0.004546815,0.01427435],"genre_scores_gemma":[0.03234046,0.01981836,0.3658567,0.1717666,0.1910934,0.0004720968,0.002989555,0.008759545,0.2069032],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.989698,"threshold_uncertainty_score":0.07035559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01586061032903796,"score_gpt":0.2686938870867465,"score_spread":0.2528332767577086,"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."}}