{"id":"W2916588339","doi":"10.1002/spe.2683","title":"Micro‐ and macro‐optimizations of S<scp>aa</scp>T search","year":2019,"lang":"en","type":"article","venue":"Software Practice and Experience","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Macro; Computer science; Search engine; Parallel computing; Baseline (sea); Programming language; Information retrieval","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":[0.0009303786,0.0009813799,0.0007265973,0.0009072354,0.0006545565,0.001288376,0.001996356,0.0006065065,0.01117779],"category_scores_gemma":[0.004492264,0.000388449,0.0007691192,0.001658848,0.0007334519,0.002120407,0.001042537,0.001234128,0.002880652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001084346,"about_ca_system_score_gemma":0.002402722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01672941,"about_ca_topic_score_gemma":0.02918411,"domain_scores_codex":[0.9984365,0.0003319733,0.0001743419,0.000243777,0.0004980398,0.000315359],"domain_scores_gemma":[0.995871,0.001850962,0.0002183741,0.001145624,0.0007097975,0.0002042042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003963821,0.001250245,0.01213028,0.0008376921,0.0003449644,0.0004016541,0.000332551,0.1583757,0.09875786,0.01175945,0.07318748,0.6386583],"study_design_scores_gemma":[0.0005402992,0.00106492,0.006289408,0.00003890035,0.0001814461,0.0002359404,0.0003033071,0.85629,0.1094337,0.007122827,0.01838283,0.000116488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6645696,0.001519,0.2002005,0.001482109,0.0005178321,0.0004600494,0.002898896,0.08907215,0.03927978],"genre_scores_gemma":[0.7659085,0.0002145345,0.2201101,0.0003971194,0.00007945395,0.0001628592,0.002499777,0.003866434,0.00676121],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01672941,"threshold_uncertainty_score":0.03739351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01491294430516134,"score_gpt":0.2894961418620294,"score_spread":0.2745831975568681,"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."}}