{"id":"W4403683610","doi":"10.29169/1927-5129.2024.20.11","title":"Fuzzy Soft Sets and its Application to Decision Making: A Short Case Study Involving the Health Sector","year":2024,"lang":"en","type":"article","venue":"Journal of Basic & Applied Sciences","topic":"Fuzzy and Soft Set Theory","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fuzzy logic; Computer science; Soft computing; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.02204477,0.000177328,0.0003799845,0.0006282839,0.0009913926,0.001046282,0.001096203,0.00004895373,0.00002867386],"category_scores_gemma":[0.0009861296,0.00009299372,0.00009447627,0.002254047,0.0002041732,0.0004938222,0.0002597258,0.0003532199,0.00005248351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008383836,"about_ca_system_score_gemma":0.0004590485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001204452,"about_ca_topic_score_gemma":0.0001686324,"domain_scores_codex":[0.9955161,0.0002632346,0.001054913,0.0005737121,0.002258871,0.0003331522],"domain_scores_gemma":[0.9945329,0.00436785,0.0003059682,0.0003264278,0.0002139732,0.0002528817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001746858,0.0002074666,0.002385197,0.00002508017,0.0000413958,0.0005803811,0.03225667,0.001572814,0.001735891,0.01815282,0.005008033,0.9378596],"study_design_scores_gemma":[0.001093039,0.004696914,0.01689837,0.0007283364,0.000144002,0.01496391,0.2948485,0.02398397,0.00050712,0.627717,0.01330817,0.001110724],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876062,0.002401917,0.005987128,0.00192807,0.0006115227,0.0006308806,0.000004260673,0.00002477981,0.0008052296],"genre_scores_gemma":[0.997849,0.00002308634,0.0009108576,0.0009403854,0.0002266654,0.00001714878,6.812133e-8,0.000009735929,0.00002299899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9367489,"threshold_uncertainty_score":0.9999907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1043706248640601,"score_gpt":0.4176053907962622,"score_spread":0.3132347659322021,"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."}}