{"id":"W2766389817","doi":"10.23638/lmcs-15(2:10)2019","title":"A Denotational Semantics for SPARC TSO","year":2019,"lang":"en","type":"preprint","venue":"Logical Methods in Computer Science","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Scheme for Promotion of Academic and Research Collaboration","keywords":"Denotational semantics of the Actor model; Normalisation by evaluation; Denotational semantics; Programming language; Computer science; Modular design; Semantics (computer science); Axiom; Principle of compositionality; Operational semantics; Theoretical computer science; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005800433,0.0003787868,0.0006213684,0.0006085263,0.0002202161,0.0008652711,0.005309213,0.0003177169,0.000004288893],"category_scores_gemma":[0.000632976,0.0003381685,0.0001950469,0.001203326,0.0003801792,0.0003411941,0.005744197,0.0007054634,0.00001025084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001859503,"about_ca_system_score_gemma":0.0006377338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008077063,"about_ca_topic_score_gemma":7.248535e-7,"domain_scores_codex":[0.9955815,0.0004852434,0.0007100787,0.001822157,0.0006950399,0.0007059989],"domain_scores_gemma":[0.995829,0.001764104,0.0003719036,0.001376985,0.0004891377,0.0001688777],"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":[0.000008007434,0.0001526107,0.0003610267,0.0001194166,0.000009643109,0.000009248195,0.0003734375,0.6605313,0.0001711811,0.1152483,0.0003805415,0.2226352],"study_design_scores_gemma":[0.0001735909,0.000105103,0.001271116,0.000108649,0.000003086569,0.0000100653,7.435488e-7,0.7942411,0.0008054601,0.2025571,0.0003685977,0.0003553976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003779544,0.000118487,0.9937708,0.001021822,0.002936413,0.0008815817,0.000004441528,0.0004847377,0.0004036971],"genre_scores_gemma":[0.01806283,0.00002571594,0.9805397,0.001005858,0.0002151869,0.00009015177,0.000005284473,0.00001353609,0.00004170478],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2222798,"threshold_uncertainty_score":0.999907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1115958262895373,"score_gpt":0.4331970286915267,"score_spread":0.3216012024019893,"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."}}