{"id":"W2418965201","doi":"10.1142/s0129626416500080","title":"Pitfalls in Memory Consistency Modelling","year":2016,"lang":"en","type":"article","venue":"Parallel Processing Letters","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Consistency (knowledge bases); Consistency model; Computer science; Sequential consistency; Data consistency; Theoretical computer science; Distributed computing; Artificial intelligence","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":[0.000383996,0.0001824701,0.000206011,0.0002297401,0.0001326867,0.0001510132,0.0007677708,0.00006363368,0.000005056856],"category_scores_gemma":[0.00002857016,0.0001380278,0.0000540909,0.0003565475,0.00008516396,0.0006784807,0.0001364467,0.0001185966,0.00003747674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007153732,"about_ca_system_score_gemma":0.0000764236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002431757,"about_ca_topic_score_gemma":0.000001930415,"domain_scores_codex":[0.9984089,0.00008061221,0.0003616114,0.0004997062,0.0002413509,0.0004077782],"domain_scores_gemma":[0.9992504,0.00009101893,0.0001399336,0.0003840222,0.00005919795,0.00007545089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004285622,0.0002493667,0.002794504,0.0001653145,0.00002895986,0.0001388468,0.003713318,0.7149994,0.01300428,0.011827,0.007653656,0.2453825],"study_design_scores_gemma":[0.0009206121,0.00002792185,0.0002629083,0.0004251134,0.000003946878,0.00002555442,0.00001474137,0.9906329,0.001304899,0.00480781,0.000993777,0.000579819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008264902,0.0003172012,0.981133,0.007466944,0.00009174648,0.0001157345,2.820729e-7,0.0006029894,0.002007203],"genre_scores_gemma":[0.6091872,0.00003084957,0.3885881,0.001975833,0.00003484681,0.00001818896,4.526477e-7,0.00001229619,0.0001522092],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6009223,"threshold_uncertainty_score":0.5628608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02377330247219279,"score_gpt":0.2452968953228121,"score_spread":0.2215235928506193,"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."}}