{"id":"W4412081561","doi":"10.1109/tcsvt.2025.3586805","title":"Style-Preserving Generator for Synthetic License Plate Recognition","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Science and Technology Council","keywords":"Computer science; License; Generator (circuit theory); Style (visual arts); Artificial intelligence; Speech recognition; Computer vision; Pattern recognition (psychology); Power (physics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002153642,0.000231351,0.0003468416,0.0007166723,0.000272238,0.00007180613,0.0001258135,0.0003910148,0.000007769498],"category_scores_gemma":[0.00002662829,0.0002528495,0.000098808,0.0003674855,0.0000476788,0.0001417436,0.000001362923,0.0002270144,0.00001454099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001048484,"about_ca_system_score_gemma":0.00002246187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001650684,"about_ca_topic_score_gemma":0.00004365987,"domain_scores_codex":[0.9987627,0.00002465194,0.0004022672,0.0003633201,0.00007758195,0.0003694361],"domain_scores_gemma":[0.9991499,0.0003084185,0.00005515062,0.0002749429,0.0001542752,0.00005736343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00013381,0.0001943195,0.00001392996,0.004626874,0.0009435558,0.000009350184,0.0002762163,0.02350593,0.2265791,0.001467315,0.001664647,0.740585],"study_design_scores_gemma":[0.00386117,0.0005069835,0.00001657436,0.001899538,0.0005391559,0.0001950512,0.0008441009,0.6745106,0.287257,0.004079025,0.02528311,0.001007736],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2279777,0.0007586266,0.7661302,0.0002016049,0.001584447,0.001617026,0.0004454462,0.001019948,0.0002649702],"genre_scores_gemma":[0.997324,0.0002662785,0.000371133,0.000051569,0.00005301616,0.001711265,0.00001347447,0.00005757703,0.0001517068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7693462,"threshold_uncertainty_score":0.9999924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02183316803923871,"score_gpt":0.233628531936184,"score_spread":0.2117953638969453,"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."}}