{"id":"W2952525141","doi":"10.48550/arxiv.1807.04479","title":"RACK: Code Search in the IDE using Crowdsourced Knowledge","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Information retrieval; Web search query; Code (set theory); Source code; Context (archaeology); Matching (statistics); Programming language; Query expansion; Search engine; World Wide Web; Database; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004027787,0.001754461,0.001289763,0.007299864,0.001320488,0.002636535,0.00229856,0.001723911,0.00711323],"category_scores_gemma":[0.0180242,0.0005375415,0.001310899,0.003674284,0.001075856,0.00477663,0.006475897,0.001301554,0.007048167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001161489,"about_ca_system_score_gemma":0.002561705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009800304,"about_ca_topic_score_gemma":0.01457906,"domain_scores_codex":[0.9956583,0.001276969,0.0002898599,0.001237667,0.001271851,0.0002653832],"domain_scores_gemma":[0.9902939,0.005341075,0.0005125158,0.002366466,0.0009765937,0.000509507],"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":[0.00187291,0.001006834,0.01287323,0.003180955,0.0004648773,0.001687876,0.004158527,0.02796208,0.02116856,0.02769603,0.2613241,0.636604],"study_design_scores_gemma":[0.0006966861,0.0004141231,0.008876092,0.0004713524,0.0001631774,0.0007282782,0.004615346,0.5843154,0.03256001,0.1167306,0.2500405,0.0003883874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07716726,0.002128215,0.6170535,0.002767477,0.000537043,0.002624992,0.0571985,0.195335,0.04518808],"genre_scores_gemma":[0.2704335,0.0007579019,0.6387317,0.0008247631,0.0001353499,0.001582901,0.07010081,0.006179927,0.01125317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009800304,"threshold_uncertainty_score":0.02379608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1599790412941395,"score_gpt":0.2546604670510763,"score_spread":0.0946814257569368,"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."}}