{"id":"W1605500262","doi":"10.1007/3-540-45049-1_34","title":"Linguistic Approximation and Semantic Adjustment in the Modeling Process","year":2000,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal","funders":"","keywords":"Computer science; Representation (politics); Meaning (existential); Expression (computer science); Set (abstract data type); Context (archaeology); Fuzzy set; Natural language processing; Fuzzy logic; Artificial intelligence; Algorithm; Theoretical computer science; Linguistics; Epistemology; Programming language","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":[0.003695852,0.0006125067,0.0008946288,0.0009732864,0.0007414614,0.002849331,0.001574348,0.001220581,0.003186082],"category_scores_gemma":[0.01118454,0.0006150805,0.001557337,0.001405836,0.002315295,0.005169342,0.001879754,0.001969012,0.0006206429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001251436,"about_ca_system_score_gemma":0.0009815351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003033696,"about_ca_topic_score_gemma":0.00202031,"domain_scores_codex":[0.9972714,0.001433249,0.0001931539,0.0004527203,0.000539115,0.0001103054],"domain_scores_gemma":[0.9976432,0.001503793,0.0001587202,0.0003613291,0.0002741608,0.00005875382],"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":[0.0001307224,0.00004671824,0.0003192976,0.0001396603,0.00005778282,0.0001520781,0.0007040651,0.1164505,0.001927378,0.8191211,0.0009563543,0.05999439],"study_design_scores_gemma":[0.00001509431,0.00003226098,0.0001389667,0.00002844622,0.00004613382,0.00005629898,0.00008940785,0.4294867,0.001118409,0.5648109,0.004152264,0.00002510389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005686769,0.0002855288,0.9899047,0.0004149818,0.000058958,0.00002728093,0.00003135532,0.0001188021,0.003471573],"genre_scores_gemma":[0.4055795,0.0008356569,0.5861708,0.0001961608,0.0001887627,0.0001609729,0.0002328191,0.0001801766,0.006455135],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003695852,"threshold_uncertainty_score":0.01954573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01676775487820814,"score_gpt":0.2341719491704273,"score_spread":0.2174041942922191,"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."}}