{"id":"W2953046944","doi":"","title":"Approximately Optimal Monitoring of Plan Preconditions","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Precondition; Plan (archaeology); Computer science; Predicate transformer semantics; Point (geometry); Markov process; Risk analysis (engineering); Mathematics; Business; Semantics (computer science); Statistics","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":[0.001443745,0.0006022789,0.0009013081,0.0004147722,0.0003109679,0.001032969,0.0007858056,0.0007803106,0.002023055],"category_scores_gemma":[0.009735185,0.0006164385,0.0005529749,0.0004000537,0.0009528493,0.001637416,0.0009033784,0.001334865,0.0001901798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001694639,"about_ca_system_score_gemma":0.002720195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008740231,"about_ca_topic_score_gemma":0.008656072,"domain_scores_codex":[0.9989952,0.0003370368,0.00004530209,0.0002513503,0.0002079388,0.000163142],"domain_scores_gemma":[0.9957513,0.003079717,0.0005280097,0.000272572,0.0002293802,0.0001391263],"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":[0.00007419881,0.00001754936,0.0005897807,0.0000488433,0.00001754675,0.00002904495,0.00004825115,0.9647182,0.0007743536,0.02176859,0.0007319627,0.01118163],"study_design_scores_gemma":[0.000008463423,0.00001065511,0.0001210174,0.000006622724,0.000004402028,0.00000504926,0.000009873891,0.9851363,0.000384977,0.01409101,0.0002183959,0.000003292636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0478086,0.0002075662,0.9478818,0.0004490255,0.00002423613,0.00005156064,0.0002117853,0.0004405241,0.00292488],"genre_scores_gemma":[0.836757,0.0002093737,0.1607859,0.00009334056,0.00002704944,0.0001306605,0.0002922875,0.00009328307,0.001611133],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008740231,"threshold_uncertainty_score":0.01737875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0532551568953811,"score_gpt":0.1648349994482763,"score_spread":0.1115798425528952,"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."}}