{"id":"W4409705379","doi":"10.3390/jimaging11050131","title":"Improvements in Image Registration, Segmentation, and Artifact Removal in ThermOcular Imaging System","year":2025,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Optical Systems and Laser Technology","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artifact (error); Computer vision; Artificial intelligence; Computer science; Image registration; Segmentation; Image segmentation; Image processing; Pattern recognition (psychology); Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000454779,0.00009053484,0.0001909589,0.0003289341,0.00002102916,0.00006265822,0.00008378887,0.00002522922,0.0000032061],"category_scores_gemma":[0.00003562546,0.00008379378,0.00002851568,0.0002190951,0.00002547039,0.0003157457,0.00001803593,0.0001993113,0.000001017215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002002334,"about_ca_system_score_gemma":0.00002095847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005591155,"about_ca_topic_score_gemma":0.00001985766,"domain_scores_codex":[0.9990742,0.00002636497,0.0005549138,0.00008680376,0.0000993644,0.0001584119],"domain_scores_gemma":[0.9997146,0.00002394069,0.00009283878,0.00008966633,0.00005163909,0.00002730044],"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":[0.00003312968,0.00006455063,0.2264862,0.0008525107,0.00008020733,0.001437997,0.0005976058,0.001837439,0.664103,0.003109563,0.0007299987,0.1006678],"study_design_scores_gemma":[0.01066682,0.00008488659,0.2305406,0.006466128,0.0001400316,0.00231451,0.01895645,0.6339825,0.08648879,0.004880932,0.004410927,0.001067369],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9739907,0.001937556,0.01918389,0.0007360459,0.0003058621,0.0001529184,0.000001035468,0.00005027698,0.003641743],"genre_scores_gemma":[0.9975948,0.00002620225,0.002287365,0.00003579392,0.00002481591,0.000003016207,4.834432e-7,0.000009617875,0.00001785944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.632145,"threshold_uncertainty_score":0.3417011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0024367180527014,"score_gpt":0.2136103451575659,"score_spread":0.2111736271048645,"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."}}