{"id":"W4416961726","doi":"10.1109/pst65910.2025.11268887","title":"Context-Aware Location De-Identification Using Denoising Diffusion","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Inpainting; Image (mathematics); Object (grammar); Consistency (knowledge bases); Pattern recognition (psychology); Probabilistic logic; Noise reduction; Digital image; Noise (video)","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":[0.0004650432,0.0007148383,0.0005792726,0.0004384245,0.0001908718,0.0005919486,0.0009810224,0.0006714864,0.0007006453],"category_scores_gemma":[0.001335597,0.0003095058,0.0006284024,0.0003270204,0.0004468858,0.0008648258,0.0008672717,0.0009125558,0.0002966048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004180552,"about_ca_system_score_gemma":0.0004609755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002268421,"about_ca_topic_score_gemma":0.00329144,"domain_scores_codex":[0.9997578,0.00003698169,0.00001001931,0.00007422025,0.00008724482,0.00003368978],"domain_scores_gemma":[0.9996434,0.00008826668,0.00007053782,0.0001039134,0.00007246286,0.00002140054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003414091,0.0001060702,0.002471613,0.0002133962,0.0001193512,0.0004314957,0.0002965475,0.465615,0.152127,0.008317783,0.003090834,0.3668695],"study_design_scores_gemma":[0.000007802322,0.0000527317,0.0004661331,0.00001093398,0.00001829901,0.0002321888,0.00002718934,0.9719234,0.02351893,0.002277614,0.001453079,0.00001165218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0376346,0.0003916073,0.9599333,0.00014645,0.00005165449,0.00003014198,0.00007373734,0.0005742344,0.001164308],"genre_scores_gemma":[0.6641986,0.0007138791,0.3301542,0.0002388442,0.00007678891,0.00005557552,0.0003361902,0.0001408462,0.004085175],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002268421,"threshold_uncertainty_score":0.004510462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02759573879075157,"score_gpt":0.3118350447472676,"score_spread":0.2842393059565161,"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."}}