{"id":"W1499997010","doi":"","title":"Removing false code dependencies to speedup software build processes","year":2003,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada); University of Toronto","funders":"","keywords":"Computer science; Preprocessor; Header; Speedup; Source code; Software; Programming language; Code (set theory); Graph; Static program analysis; Parallel computing; Software development; Theoretical computer science; Set (abstract data type)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.000442685,0.0001614949,0.0001476091,0.000210303,0.0001164943,0.0002478649,0.001129677,0.00005740457,0.00006817825],"category_scores_gemma":[0.01001217,0.0001464425,0.00003339085,0.001207788,0.00002084095,0.0004669315,0.0003045943,0.0001699874,0.0004206666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007920885,"about_ca_system_score_gemma":0.0002930589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003533943,"about_ca_topic_score_gemma":0.00006111785,"domain_scores_codex":[0.9981891,0.00003326553,0.0001761445,0.0004837854,0.0005886019,0.0005290869],"domain_scores_gemma":[0.9979784,0.0007942488,0.00002217678,0.0006834255,0.0002729866,0.0002487635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002514809,0.000491551,0.761208,0.001270415,0.0001441828,0.0006990515,0.00637866,0.02400646,0.01199567,0.09361183,0.03936191,0.06080714],"study_design_scores_gemma":[0.001612607,0.0008005179,0.1241484,0.0006003487,0.00002239962,0.0008469337,0.0006666511,0.004398476,0.6421987,0.01101015,0.2097615,0.00393327],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1058852,0.0002215169,0.891229,0.0003420313,0.0002350644,0.0001902069,0.000001217013,0.001062467,0.0008333513],"genre_scores_gemma":[0.5195456,0.000008620064,0.4772432,0.0003230148,0.00003660462,0.00002401288,4.104968e-7,0.00002182927,0.002796679],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6370596,"threshold_uncertainty_score":0.9983269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02459216863320896,"score_gpt":0.2754633940127593,"score_spread":0.2508712253795504,"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."}}