{"id":"W2001390679","doi":"10.4018/jcini.2008040106","title":"Deductive Semantics of RTPA","year":2008,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Well-founded semantics; Computer science; Action semantics; Programming language; Operational semantics; Denotational semantics; Computational semantics; Formal semantics (linguistics); Semantics (computer science)","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":[0.0002843081,0.0001173175,0.000205782,0.0002242665,0.00005807662,0.00004743011,0.0006012974,0.0000427149,0.00001039808],"category_scores_gemma":[0.0004389198,0.00009511216,0.0001012307,0.0002110129,0.0002045555,0.000710149,0.000282127,0.0003081908,0.000006689774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000022637,"about_ca_system_score_gemma":0.00007695942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003664684,"about_ca_topic_score_gemma":6.830107e-7,"domain_scores_codex":[0.9985854,0.00003196339,0.0006873449,0.00007101722,0.000497691,0.000126601],"domain_scores_gemma":[0.9961368,0.0006189011,0.0006970412,0.00006515102,0.002412463,0.00006961852],"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":[0.0002573738,0.0002214402,0.004794635,0.00003034656,0.0005743909,0.0002107291,0.0162484,0.000679798,0.0001673752,0.0134384,0.0005725462,0.9628046],"study_design_scores_gemma":[0.002753192,0.00233748,0.05421048,0.004218937,0.000171442,0.01870026,0.005697718,0.7233912,0.1415197,0.04331812,0.002186138,0.001495309],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.348799,0.0005220573,0.6487847,0.0001478978,0.0009144118,0.00004912184,0.000003472894,0.000008153392,0.0007711711],"genre_scores_gemma":[0.9802143,0.001067006,0.0183064,0.0002549776,0.0001214175,4.124956e-7,0.000001373269,0.000003395432,0.00003069785],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9613093,"threshold_uncertainty_score":0.3878562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0199850913634067,"score_gpt":0.2838021070791773,"score_spread":0.2638170157157706,"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."}}