{"id":"W75118506","doi":"10.5220/0003270200770086","title":"RESOLVING ARTIFACT DESCRIPTION AMBIGUITIES DURING SOFTWARE DESIGN USING SEMIOTIC AGENT MODELLING","year":2010,"lang":"en","type":"article","venue":"","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artifact (error); Computer science; Semiotics; Software; Software design; Software engineering; Artificial intelligence; Programming language; Software development; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005881605,0.0003054427,0.0003068723,0.0004934254,0.0007460536,0.0009978079,0.0002731087,0.0001376968,0.0002717677],"category_scores_gemma":[0.0001715126,0.0002844737,0.0001492111,0.0006062032,0.0000600642,0.002302465,0.0001532614,0.000342375,0.0001122913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004244101,"about_ca_system_score_gemma":0.00003430414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001521532,"about_ca_topic_score_gemma":0.00008759204,"domain_scores_codex":[0.9981654,0.00001265285,0.0004558068,0.0004802388,0.0003749592,0.0005109998],"domain_scores_gemma":[0.99898,0.00003670037,0.0002415496,0.0003514191,0.0003637458,0.00002654989],"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":[0.00002975526,0.00006025672,0.004349015,0.0003447626,0.00004612585,0.00001293617,0.00007544991,0.9616048,0.03095514,0.0006304492,0.00008058821,0.00181075],"study_design_scores_gemma":[0.0002223945,0.000001665469,0.0002737222,0.000121776,0.0001617546,0.00000580268,0.0002174467,0.9938778,0.00135492,0.00318256,0.0001743765,0.0004057702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4914975,0.0000433679,0.5074804,0.00007848576,0.0002783369,0.00007885735,2.928125e-7,0.0003165852,0.0002261958],"genre_scores_gemma":[0.9427567,0.00001198058,0.05526743,0.0002731933,0.001126927,0.0000098818,0.00001087294,0.00006373133,0.0004793483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4522129,"threshold_uncertainty_score":0.9999607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07508093050586302,"score_gpt":0.2297671180888839,"score_spread":0.1546861875830209,"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."}}