{"id":"W2023385229","doi":"10.1142/s0218194010004931","title":"FORMALIZATION OF TEXTUAL USE CASES BASED ON PETRI NETS","year":2010,"lang":"en","type":"article","venue":"International Journal of Software Engineering and Knowledge Engineering","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Petri net; Programming language; Semantics (computer science); Syntax; Tuple; Specification language; Process architecture; Formal specification; Formal methods; Artificial intelligence; Natural language processing; Software engineering","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":[0.004912429,0.001232713,0.0005932169,0.00262997,0.00098965,0.004958091,0.002271046,0.001293474,0.004556745],"category_scores_gemma":[0.01019073,0.000890726,0.002208148,0.001562715,0.004491477,0.005027624,0.002135015,0.002839366,0.000858701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002041006,"about_ca_system_score_gemma":0.003026879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005969565,"about_ca_topic_score_gemma":0.00404464,"domain_scores_codex":[0.99314,0.002240876,0.00107321,0.0007880658,0.002293435,0.0004643458],"domain_scores_gemma":[0.9924085,0.004333213,0.001069766,0.0007360531,0.001262309,0.0001902429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004030945,0.0000891824,0.0004599817,0.0002127635,0.00002924448,0.0009761109,0.0009897857,0.04683,0.003795691,0.9239721,0.001156884,0.02144789],"study_design_scores_gemma":[0.00008840333,0.00008174046,0.0004051642,0.0005007874,0.00008751567,0.0009204647,0.0003874122,0.3653456,0.012415,0.5610782,0.05859639,0.00009329785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003642885,0.0001432068,0.989448,0.0002709576,0.00005492849,0.0002615738,0.0001837338,0.0005671302,0.005427557],"genre_scores_gemma":[0.1765818,0.0008053652,0.8133622,0.0002843876,0.000166215,0.001184976,0.001092771,0.0002427611,0.006279529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005969565,"threshold_uncertainty_score":0.0259797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0111263678228046,"score_gpt":0.218509957292181,"score_spread":0.2073835894693764,"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."}}