{"id":"W4236483261","doi":"10.22360/springsim.2017.tmsdevs.018","title":"A Taxonomy of Event Time Representations","year":2017,"lang":"en","type":"article","venue":"","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Autodesk (Canada)","funders":"","keywords":"Computer science; Event (particle physics); Event structure; Debugging; Categorization; Context (archaeology); Taxonomy (biology); Vocabulary; Natural language processing; Theoretical computer science; Causal consistency; Consistency (knowledge bases); Artificial intelligence; Correctness; Programming language; Consistency model; Linguistics","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":[0.004974855,0.001579124,0.0009506303,0.007638326,0.00182245,0.009487878,0.003140626,0.00268571,0.008827399],"category_scores_gemma":[0.01706729,0.0007388741,0.001713287,0.010638,0.00342463,0.01511676,0.002603136,0.004271991,0.002490535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002634939,"about_ca_system_score_gemma":0.002826165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003072976,"about_ca_topic_score_gemma":0.001523852,"domain_scores_codex":[0.9945176,0.001752691,0.001117568,0.0008429323,0.001445939,0.000323222],"domain_scores_gemma":[0.9908202,0.004585146,0.00110226,0.001488442,0.001657112,0.0003467674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001587538,0.00001312583,0.0002018934,0.0002099922,0.000009068485,0.00006623295,0.0004974748,0.001981469,0.0002102411,0.9712802,0.003122658,0.02239177],"study_design_scores_gemma":[0.00001493648,0.00003776556,0.0002831846,0.0005566124,0.00002724346,0.0005447005,0.0006140037,0.01722771,0.0005654215,0.7370315,0.2430489,0.00004804817],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004592933,0.01175026,0.9237897,0.003498532,0.0009142647,0.0003641917,0.001645601,0.001294717,0.05214984],"genre_scores_gemma":[0.1331193,0.02500622,0.8169241,0.00168067,0.001310083,0.001582553,0.004601231,0.0005602289,0.01521568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009487878,"threshold_uncertainty_score":0.02953058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2841104918975135,"score_gpt":0.5012982917861731,"score_spread":0.2171877998886597,"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."}}