{"id":"W2518214351","doi":"10.1145/2970276.2970348","title":"Migrating cascading style sheets to preprocessors by introducing mixins","year":2016,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Maintainability; Style sheet; Cascading Style Sheets; Programming language; Code (set theory); Preprocessor; Code reuse; Semantics (computer science); Software engineering; World Wide Web; XML; Web page; Software","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00246882,0.002046671,0.0006911245,0.002225738,0.0006776035,0.002522678,0.001575878,0.001103358,0.002401213],"category_scores_gemma":[0.01620662,0.001383861,0.001416084,0.001230146,0.001101199,0.0035312,0.002730694,0.001994356,0.002153126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007754014,"about_ca_system_score_gemma":0.001343053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001210823,"about_ca_topic_score_gemma":0.001230574,"domain_scores_codex":[0.9967628,0.0004192967,0.00045992,0.0008447507,0.001321862,0.0001913593],"domain_scores_gemma":[0.9797534,0.005946668,0.003265936,0.00818983,0.002307914,0.0005361465],"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":[0.0008838502,0.0003363625,0.03902176,0.0008064838,0.0002556517,0.00218472,0.003547593,0.01210963,0.238188,0.01164265,0.01231947,0.6787038],"study_design_scores_gemma":[0.00009216191,0.000468937,0.01302989,0.0003015016,0.0002899151,0.002719488,0.0005784743,0.2085079,0.6364879,0.01700075,0.1201939,0.0003292113],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1288091,0.0004666496,0.7736955,0.0002487132,0.0001913377,0.0005593658,0.0003689155,0.09208541,0.003575009],"genre_scores_gemma":[0.2652029,0.0003665579,0.7111655,0.0003927214,0.00008636779,0.0002865823,0.001007447,0.01291631,0.008575597],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002522678,"threshold_uncertainty_score":0.01305658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009549300685594193,"score_gpt":0.2525183850635264,"score_spread":0.2429690843779322,"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."}}