{"id":"W2165821496","doi":"10.1002/cpe.3263","title":"High‐performance<i>N</i>‐thread software solutions for mutual exclusion","year":2014,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Distributed systems and fault tolerance","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Mutual exclusion; Correctness; Thread (computing); Intuition; Algorithm; Software; Implementation; Critical section; Parallel computing; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001734148,0.0005352285,0.0005303798,0.0006616436,0.0007025289,0.001981251,0.001862947,0.0007910132,0.005785853],"category_scores_gemma":[0.004233603,0.0003058562,0.0006109713,0.0006414934,0.0009414038,0.002254572,0.001698485,0.001220144,0.001676906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001255666,"about_ca_system_score_gemma":0.001402276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001284741,"about_ca_topic_score_gemma":0.0009640519,"domain_scores_codex":[0.998694,0.0003180939,0.0001052395,0.0001529924,0.0005574782,0.000172127],"domain_scores_gemma":[0.9976534,0.0007114115,0.000288037,0.0005909042,0.0005978503,0.0001584031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003490252,0.0002617256,0.002913465,0.0006469055,0.00006412786,0.0001547115,0.0005833278,0.08247323,0.03332866,0.3045303,0.02050887,0.5541856],"study_design_scores_gemma":[0.0001329525,0.0003211142,0.0007888126,0.0001729064,0.00004711094,0.0003535884,0.0001571247,0.7355641,0.05567936,0.1345439,0.0721612,0.00007781309],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01796274,0.0005960909,0.9697651,0.0004700633,0.000126907,0.0000824079,0.00002835326,0.003335256,0.00763305],"genre_scores_gemma":[0.3063053,0.0006217868,0.6812845,0.0002349121,0.0001203186,0.0002208755,0.0002029138,0.0008249577,0.01018442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005785853,"threshold_uncertainty_score":0.0193556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02517790342176533,"score_gpt":0.2955406178304535,"score_spread":0.2703627144086881,"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."}}