{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006337482,0.0001567485,0.0002093385,0.0001358901,0.0005783113,0.0001470785,0.001413429,0.00003672161,0.0001183525],"category_scores_gemma":[0.00007259995,0.0001658302,0.0001216026,0.0009424452,0.00004147248,0.0004094597,0.0006302256,0.0002028411,0.0005425566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001475325,"about_ca_system_score_gemma":0.00004244582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008166904,"about_ca_topic_score_gemma":0.000004076144,"domain_scores_codex":[0.9976524,0.0002251778,0.0005307678,0.0006530337,0.0005256501,0.0004130354],"domain_scores_gemma":[0.9984961,0.0001053952,0.000205773,0.0009914879,0.00009100714,0.000110211],"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":[0.00009591389,0.0009228691,0.003553441,0.00006306529,0.00008544699,0.0002052612,0.01073663,0.1621581,0.01012215,0.5958205,0.009518098,0.2067185],"study_design_scores_gemma":[0.0003772618,0.0006221934,0.0003532475,0.00003955002,0.00002142688,0.0001530681,0.005066892,0.7274672,0.01175566,0.2105206,0.04283137,0.0007914815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03271143,0.0009887918,0.8176065,0.002390318,0.003562894,0.001030772,0.000008355008,0.0004668154,0.1412341],"genre_scores_gemma":[0.9926744,0.00001801007,0.001601705,0.0003749828,0.0001045302,0.0002056399,0.00001088033,0.00001212089,0.004997725],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.959963,"threshold_uncertainty_score":0.6973648,"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."}}