{"id":"W2006432698","doi":"10.1145/2552999.2553002","title":"Parallelism in Ada","year":2013,"lang":"en","type":"article","venue":"ACM SIGAda Ada Letters","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Dynamics (Canada)","funders":"","keywords":"Computer science; Task parallelism; Parallelism (grammar); Concurrency; Parallel computing; Instruction-level parallelism; Implicit parallelism; Data parallelism; Task (project management); Divide and conquer algorithms; Syntax; sort; Programming language","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":[0.0001664078,0.0001620703,0.000169205,0.0001612368,0.00007038079,0.0001945499,0.001693446,0.00006015545,0.00004158608],"category_scores_gemma":[0.00009244365,0.0001561906,0.00005209221,0.0004011894,0.00003631881,0.0005909835,0.0004429642,0.0001937852,0.0001659078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004947186,"about_ca_system_score_gemma":0.00002800449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006819274,"about_ca_topic_score_gemma":0.00001455771,"domain_scores_codex":[0.9986362,0.00008113263,0.0002779113,0.0003860713,0.0002488206,0.0003698761],"domain_scores_gemma":[0.9987084,0.000162587,0.00008063873,0.0009231119,0.00004020695,0.00008504243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009729662,0.0002226725,0.01323684,0.00005008987,0.00005140909,0.000187578,0.003016416,0.04701595,0.01523619,0.0125666,0.8362955,0.07211103],"study_design_scores_gemma":[0.004016687,0.000347881,0.1002411,0.0004747403,0.00001748912,0.0001338543,0.0001825165,0.7258253,0.02056029,0.05981793,0.08419896,0.004183219],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1871935,0.0001212062,0.7722168,0.03741035,0.0003262383,0.0003483561,3.793677e-7,0.0008014766,0.001581714],"genre_scores_gemma":[0.6422933,0.0000163358,0.3431539,0.01427128,0.00005816019,0.00005388252,0.000002618531,0.00001232718,0.0001381973],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7520965,"threshold_uncertainty_score":0.6369269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01202024888399344,"score_gpt":0.2255274574977706,"score_spread":0.2135072086137772,"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."}}