{"id":"W4380538756","doi":"10.1117/12.2663482","title":"Sensitivity of infrared ship signature analysis to climatic data sampling methods","year":2023,"lang":"en","type":"article","venue":"","topic":"Marine and Coastal Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"BGC Engineering (Canada)","funders":"","keywords":"Buoy; Data set; Combatant; Sensitivity (control systems); Sea surface temperature; Sampling (signal processing); Signature (topology); Environmental data; Meteorology; Remote sensing; Environmental science; Uncorrelated; Computer science; Statistics; Oceanography; Mathematics; Geology; Geography; Engineering; Telecommunications","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":[0.02019353,0.0008435465,0.0005738657,0.001826844,0.0005554074,0.001442207,0.0008905538,0.0007735766,0.0008020565],"category_scores_gemma":[0.07757476,0.0003904226,0.0009736217,0.001814237,0.0007920661,0.001632686,0.001795029,0.0009306982,0.0003994076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007102827,"about_ca_system_score_gemma":0.0005242492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004116025,"about_ca_topic_score_gemma":0.003037938,"domain_scores_codex":[0.9857587,0.007457562,0.001008758,0.002446207,0.002955189,0.0003737093],"domain_scores_gemma":[0.9002164,0.07799552,0.007155253,0.009448091,0.004629592,0.0005550489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001321676,0.0002552176,0.6827279,0.0004502236,0.001437082,0.0003576242,0.001138111,0.1053513,0.03911842,0.001461494,0.001617917,0.164763],"study_design_scores_gemma":[0.00006417264,0.000680041,0.6408648,0.0001562206,0.000536604,0.001097686,0.001172293,0.294339,0.05281014,0.003304818,0.004782496,0.000191636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8598554,0.001879043,0.131328,0.0005051717,0.0002816865,0.0002534646,0.001012111,0.0005575933,0.004327446],"genre_scores_gemma":[0.9655613,0.0004366824,0.03127029,0.0003256481,0.0001044391,0.00007010077,0.001368369,0.0001701143,0.0006932026],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02019353,"threshold_uncertainty_score":0.1067949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1375735744142097,"score_gpt":0.4212908106454695,"score_spread":0.2837172362312598,"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."}}