{"id":"W2529518761","doi":"","title":"TLQSolver: A Temporal Logic Query Checker","year":2003,"lang":"en","type":"article","venue":"","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Temporal logic; Model checking; Query optimization; A priori and a posteriori; Symbol (formal); Boolean conjunctive query; Propositional calculus; Theoretical computer science; Query language; Sargable; Algorithm; Programming language; Web search query; Information retrieval; Search engine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005337418,0.001312264,0.001575577,0.002954778,0.00123601,0.005371137,0.004640916,0.00219243,0.04109139],"category_scores_gemma":[0.01592621,0.001558169,0.002249891,0.002316666,0.001940435,0.01049626,0.005416558,0.002330446,0.007613179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00198795,"about_ca_system_score_gemma":0.003120017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007980203,"about_ca_topic_score_gemma":0.006183289,"domain_scores_codex":[0.995201,0.0009706073,0.0007443586,0.0009154,0.001774664,0.0003939406],"domain_scores_gemma":[0.9918995,0.004922359,0.0004028938,0.001524278,0.001038009,0.0002129528],"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":[0.002830743,0.0004550501,0.003471463,0.002743012,0.0004555922,0.001281122,0.001448288,0.02942086,0.03044185,0.2995992,0.2063652,0.4214876],"study_design_scores_gemma":[0.0008754099,0.0002159492,0.0005405636,0.0003761072,0.000450245,0.0005939699,0.0008011408,0.4626811,0.08114461,0.2192668,0.2328244,0.0002295595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006109269,0.0002856237,0.8433052,0.000908325,0.000290221,0.0004132884,0.007299742,0.1340003,0.007388058],"genre_scores_gemma":[0.3010609,0.0008175988,0.6164834,0.003073213,0.0003287743,0.0008273527,0.02103234,0.03303424,0.02334212],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04109139,"threshold_uncertainty_score":0.1374643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05231091551645177,"score_gpt":0.2462077570863371,"score_spread":0.1938968415698853,"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."}}