{"id":"W4409736273","doi":"10.1145/3706598.3713822","title":"Code Shaping: Iterative Code Editing with Free-form AI-Interpreted Sketching","year":2025,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Programming language; Code (set theory); Computer graphics (images)","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":[0.001059245,0.001211923,0.0007164294,0.000774144,0.00078604,0.002507448,0.002717251,0.001273167,0.02100654],"category_scores_gemma":[0.007488491,0.0007919008,0.00115018,0.0005590338,0.00178821,0.00187657,0.00413844,0.001858625,0.003595143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004230903,"about_ca_system_score_gemma":0.00059437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001392855,"about_ca_topic_score_gemma":0.001571295,"domain_scores_codex":[0.9988012,0.000254018,0.00006052571,0.0002150574,0.0005490426,0.0001202025],"domain_scores_gemma":[0.9964972,0.001806776,0.0001003277,0.0009759559,0.0004505945,0.000169183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007483765,0.0002604649,0.001116688,0.0007220746,0.0001282867,0.0007406204,0.005020231,0.07345649,0.1789597,0.0991105,0.01378557,0.6259511],"study_design_scores_gemma":[0.0001587977,0.0001974638,0.0004313342,0.0001132988,0.00005698511,0.0005332215,0.0005134927,0.7331899,0.1567192,0.04867339,0.0592619,0.0001510028],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006625165,0.00003868109,0.9789489,0.00004721515,0.00005668671,0.00007020614,0.00004766267,0.009270418,0.004895164],"genre_scores_gemma":[0.2592904,0.0001366019,0.7229527,0.0001273683,0.00003544686,0.0001708324,0.0002851365,0.005216549,0.01178496],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02100654,"threshold_uncertainty_score":0.07027382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01343686541416983,"score_gpt":0.2832070046406083,"score_spread":0.2697701392264385,"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."}}