{"id":"W2786902291","doi":"10.1002/cpe.4475","title":"High‐contention mutual exclusion by elevator algorithms","year":2018,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Distributed systems and fault tolerance","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Mutual exclusion; Thread (computing); Critical section; Algorithm; Software; Parallel computing; Concurrency; Operating system; Theoretical computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002568072,0.0001431416,0.000149121,0.00003967479,0.0004144218,0.0003357235,0.0002022535,0.0000634245,0.000009265088],"category_scores_gemma":[0.000131238,0.0001334021,0.00001889056,0.0002504646,0.0001756019,0.002205939,0.000141717,0.0001018756,0.00002729701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001384825,"about_ca_system_score_gemma":0.00003197813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007630607,"about_ca_topic_score_gemma":0.000001360974,"domain_scores_codex":[0.9987032,0.0001012106,0.0002810222,0.000469883,0.0002410263,0.0002036378],"domain_scores_gemma":[0.9990924,0.0001463584,0.0001961097,0.0001629248,0.0002796375,0.0001225688],"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":[0.0000442013,0.0001666815,0.0002464333,0.00003084401,0.00002132791,0.00001473286,0.01464359,0.000004785849,0.003746728,0.05349764,0.007132148,0.9204509],"study_design_scores_gemma":[0.003893359,0.001913874,0.003798394,0.0002857166,0.00004906315,0.0005760054,0.009797693,0.5488616,0.00613768,0.005280062,0.417868,0.001538567],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.150143,0.00144425,0.8459554,0.0008056207,0.0009253421,0.0001398171,0.000009981447,0.00009163855,0.0004849398],"genre_scores_gemma":[0.9918289,0.0001918366,0.007198102,0.000525147,0.0001177889,0.00002383696,0.000016333,0.000004380366,0.00009369809],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9189123,"threshold_uncertainty_score":0.5439981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01676340393761701,"score_gpt":0.307545427721807,"score_spread":0.29078202378419,"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."}}