{"id":"W4280590125","doi":"10.18280/ria.360210","title":"Fuzzy Deep Daily Nutrients Requirements Representation","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Centre National pour la Recherche Scientifique et Technique","keywords":"Encoder; Computer science; Artificial neural network; Fuzzy logic; Crossover; Artificial intelligence; Machine learning; Autoencoder; Representation (politics); Genetic algorithm; Data mining; Population; Fuzzy number; Encoding (memory); Fuzzy set","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004801505,0.0005932768,0.0004656953,0.0008103942,0.000270812,0.001123594,0.001198669,0.0009202715,0.003913851],"category_scores_gemma":[0.001972749,0.00028469,0.0006200177,0.0005991348,0.0002821529,0.001099636,0.0004341334,0.0006355182,0.0005464444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001144194,"about_ca_system_score_gemma":0.0009467338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01188618,"about_ca_topic_score_gemma":0.009684025,"domain_scores_codex":[0.999611,0.0000596867,0.00003410918,0.0001396673,0.000111302,0.00004429029],"domain_scores_gemma":[0.999599,0.0001322925,0.00005450707,0.00005778452,0.0001407691,0.00001575618],"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":[0.0003013175,0.0001532769,0.00488566,0.0001569614,0.00007293421,0.0001750202,0.0002142883,0.7152355,0.009784638,0.02261774,0.003988698,0.2424139],"study_design_scores_gemma":[0.000008261947,0.00002317507,0.0006556266,0.000009183563,0.00001137837,0.0000299175,0.00001707359,0.9878547,0.002246332,0.007886949,0.001246113,0.00001132295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05928948,0.0002074234,0.928386,0.000357558,0.000051775,0.00007978502,0.001041044,0.001509761,0.009077109],"genre_scores_gemma":[0.8005351,0.0002057,0.1921603,0.0001833712,0.00002498856,0.0002170666,0.0009154671,0.00007974656,0.005678273],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01188618,"threshold_uncertainty_score":0.02363402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05144945227523112,"score_gpt":0.2812948883984254,"score_spread":0.2298454361231942,"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."}}