{"id":"W2911938026","doi":"10.1007/s10766-019-00631-4","title":"Efficient Methods for Trace Analysis Parallelization","year":2019,"lang":"en","type":"article","venue":"International Journal of Parallel Programming","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Speedup; Scalability; Parallel computing; TRACE (psycholinguistics); Parallelizable manifold; Tracing; Synchronization (alternating current); Thread (computing); Distributed computing; Algorithm; Channel (broadcasting); Database; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008987595,0.0001247293,0.0002747911,0.0006074382,0.00004001273,0.000192165,0.001790016,0.00006423655,0.00001130647],"category_scores_gemma":[0.0003464477,0.0001076117,0.0002933749,0.0005744571,0.00003334096,0.0004242966,0.0002036813,0.0001489085,0.00000831743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001204576,"about_ca_system_score_gemma":0.00005271332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002791465,"about_ca_topic_score_gemma":0.000001385607,"domain_scores_codex":[0.9985361,0.00005943351,0.0005349635,0.0002421282,0.0004198668,0.0002075389],"domain_scores_gemma":[0.9979923,0.0002791088,0.0006633436,0.0003110935,0.0007011133,0.00005305953],"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":[0.00005338053,0.0001157735,0.000900807,0.000007667116,0.000740934,0.0000150487,0.0002527109,0.3672475,0.0007462803,0.04455069,0.00004873523,0.5853205],"study_design_scores_gemma":[0.001285664,0.0002792325,0.0006801107,0.00003420004,0.0001374761,0.0000893773,0.0001487267,0.9232045,0.0009908361,0.009239056,0.06364477,0.0002660809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006741174,0.0004988263,0.9905738,0.0009879917,0.000828406,0.0002298555,0.000002319427,0.00008862608,0.00004902103],"genre_scores_gemma":[0.2356824,0.00001700431,0.7641225,0.00004424141,0.00005566902,0.00001180479,0.000004906861,0.000006395541,0.00005498678],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5850544,"threshold_uncertainty_score":0.4388278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.023685949324013,"score_gpt":0.3733582132010854,"score_spread":0.3496722638770723,"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."}}