{"id":"W7125984020","doi":"10.1109/ase63991.2025.00137","title":"Characterizing Multi-Hunk Patches: Divergence, Proximity, and LLM Repair Challenges","year":2025,"lang":"","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of British Columbia","funders":"","keywords":"Metric (unit); Disjoint sets; Divergence (linguistics); Empirical research; Variation (astronomy); Event (particle 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006454756,0.0009753499,0.001028983,0.003642349,0.001009885,0.001792302,0.002200646,0.00166724,0.0008131493],"category_scores_gemma":[0.05159881,0.0005088734,0.0009501287,0.002794059,0.001823992,0.004386987,0.002857303,0.002225999,0.0004873303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001222703,"about_ca_system_score_gemma":0.00142668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005835345,"about_ca_topic_score_gemma":0.009242274,"domain_scores_codex":[0.9942966,0.001297477,0.0004371343,0.001998556,0.001615611,0.0003546533],"domain_scores_gemma":[0.9509625,0.02740288,0.006543852,0.01072092,0.003162035,0.001207789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008631826,0.0005521677,0.5187277,0.001478027,0.0004068712,0.0008125431,0.003103239,0.2225126,0.008665747,0.00905852,0.01596373,0.2178558],"study_design_scores_gemma":[0.0001011971,0.0006421268,0.1239953,0.0002521139,0.00015414,0.002152519,0.00242915,0.8063802,0.007543573,0.04429926,0.01192799,0.0001224097],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8748052,0.002747508,0.1109317,0.0009782866,0.00008398095,0.000154506,0.00363685,0.004290432,0.002371631],"genre_scores_gemma":[0.946671,0.0003532513,0.04464101,0.0001748834,0.00003556122,0.0001128322,0.006751755,0.0005405613,0.0007191309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006454756,"threshold_uncertainty_score":0.03413641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04611034680193843,"score_gpt":0.2915173091313208,"score_spread":0.2454069623293824,"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."}}