{"id":"W7111132206","doi":"10.5281/zenodo.17862616","title":"Implementing Single Image Denoising Diffusion Model for Image Editing and Synthesis","year":2025,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Image editing; Image (mathematics); Noise reduction; Range (aeronautics); Prior probability; Generative grammar; Image synthesis; Image manipulation","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":[0.001125997,0.0007630912,0.0005755416,0.000371677,0.0002217328,0.0007568441,0.001226583,0.0009642728,0.002243895],"category_scores_gemma":[0.002079047,0.0003865941,0.0006955022,0.0002498273,0.0006512394,0.0009113511,0.001166508,0.001388396,0.0009753821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006137748,"about_ca_system_score_gemma":0.0006139559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001952643,"about_ca_topic_score_gemma":0.002537627,"domain_scores_codex":[0.9995908,0.00008173389,0.00001513819,0.0001158988,0.0001616756,0.00003475151],"domain_scores_gemma":[0.9994569,0.0002566662,0.00004162543,0.0001341281,0.00008070223,0.00003002001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002156077,0.0001066975,0.0006265374,0.0001503325,0.0000873425,0.0001318156,0.0001327048,0.7053032,0.07567757,0.01924803,0.001612617,0.1967075],"study_design_scores_gemma":[0.000005823154,0.00002886862,0.00005934629,0.000004390654,0.0000063588,0.00004766576,0.000005503025,0.9838964,0.01253782,0.002333347,0.001067431,0.000007091927],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00804754,0.0001323418,0.9893708,0.00007979028,0.000027156,0.00003600507,0.00003420236,0.0007431852,0.00152904],"genre_scores_gemma":[0.4069001,0.0003828834,0.5835868,0.0002014547,0.00004243186,0.0001321273,0.0002630786,0.0004044185,0.008086659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002243895,"threshold_uncertainty_score":0.007506609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03060336042419682,"score_gpt":0.2540704234100435,"score_spread":0.2234670629858467,"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."}}