{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01468723,0.001415463,0.001338858,0.002900633,0.002154012,0.008004013,0.002702938,0.002799283,0.002144682],"category_scores_gemma":[0.04805582,0.002397969,0.002445794,0.001718908,0.003686541,0.00913676,0.006008637,0.003441917,0.0005219971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002031576,"about_ca_system_score_gemma":0.003662843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003321769,"about_ca_topic_score_gemma":0.003984317,"domain_scores_codex":[0.9768683,0.01331252,0.001993194,0.001293894,0.005799712,0.0007323832],"domain_scores_gemma":[0.9556927,0.03030145,0.002927548,0.006887062,0.00377341,0.0004178286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006521292,0.0003228879,0.006093746,0.001500039,0.0003713556,0.003707698,0.03601429,0.184226,0.03341962,0.4246533,0.002458301,0.3065806],"study_design_scores_gemma":[0.00006875807,0.0001425205,0.0004614409,0.0004433523,0.0002871316,0.0008305137,0.003566909,0.732317,0.04370643,0.1906355,0.02740879,0.0001316646],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01369332,0.000080575,0.9836152,0.0002101477,0.00002185522,0.00009826587,0.00002198693,0.0005459454,0.001712724],"genre_scores_gemma":[0.3014917,0.0001921632,0.6959054,0.00009799492,0.00001432741,0.0001998608,0.0001863462,0.0004704303,0.001441815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01468723,"threshold_uncertainty_score":0.07767439,"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."}}