{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000297503,0.00009440748,0.0001207882,0.00008953526,0.0001536595,0.0001967346,0.0003053966,0.00005365326,0.00001941863],"category_scores_gemma":[0.0006358149,0.00008411767,0.00002153074,0.0003660952,0.0001219058,0.002526377,0.0003145262,0.0001457915,0.00004827175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001126756,"about_ca_system_score_gemma":0.00008162304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005040358,"about_ca_topic_score_gemma":5.729264e-7,"domain_scores_codex":[0.9989619,0.00005517266,0.0002012039,0.0002367653,0.0003137443,0.0002312716],"domain_scores_gemma":[0.9986233,0.0005884414,0.00009717471,0.0002967275,0.0002796508,0.0001147431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001090418,0.0007714608,0.1716637,0.0007259941,0.0001013808,0.00008472487,0.5281843,0.0006517716,0.04403706,0.06847496,0.002064563,0.1831311],"study_design_scores_gemma":[0.007429643,0.003651323,0.1373658,0.0005317332,0.0001418681,0.002330883,0.1662104,0.07570778,0.3457441,0.002089642,0.2568966,0.001900158],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8470588,0.0003366062,0.150667,0.0004113443,0.0001369626,0.0002517432,0.000006362622,0.00007847453,0.001052737],"genre_scores_gemma":[0.8194596,0.0005094885,0.1782083,0.0007117433,0.00001651588,0.00001964422,0.000004594225,0.000008039457,0.001062084],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3619739,"threshold_uncertainty_score":0.3430219,"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."}}