{"id":"W1976806144","doi":"10.1371/journal.pone.0066523","title":"Computational Models for Prediction of Yeast Strain Potential for Winemaking from Phenotypic Profiles","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Fermentation and Sensory Analysis","field":"Agricultural and Biological Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; Fundação para a Ciência e a Tecnologia; Centro de Estudos Ambientais e Marinhos, Universidade de Aveiro","keywords":"Winemaking; Biology; Phenotype; Strain (injury); Saccharomyces cerevisiae; Yeast; Genetics; Biotechnology; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.002219351,0.001103353,0.001364933,0.001469069,0.0005798009,0.001593687,0.001593499,0.001710163,0.002294984],"category_scores_gemma":[0.007514752,0.0007876978,0.001244041,0.0009103228,0.0005442929,0.000932246,0.0009054086,0.001549273,0.0003462775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001240712,"about_ca_system_score_gemma":0.001480918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01383103,"about_ca_topic_score_gemma":0.01079921,"domain_scores_codex":[0.9995549,0.0001904839,0.00003822912,0.0001067792,0.00004739545,0.00006207234],"domain_scores_gemma":[0.991488,0.00759571,0.0002842965,0.0001049966,0.0003698179,0.0001572384],"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":[0.00006884924,0.00004785403,0.00199937,0.00003785757,0.00003401761,0.00003527775,0.00001328336,0.9904578,0.00007455988,0.000885022,0.0004121892,0.005933817],"study_design_scores_gemma":[0.000003168492,0.000004231415,0.00006742242,0.000002883615,0.000002801234,0.00000193446,0.000002991645,0.999111,0.00001640485,0.0007558136,0.00003021696,0.000001030896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5128798,0.003063941,0.4664278,0.00338827,0.0002608383,0.0003106642,0.004563242,0.002289491,0.006815915],"genre_scores_gemma":[0.9186155,0.000593521,0.07400708,0.0002931632,0.0001048129,0.0005425139,0.003464635,0.00008662995,0.002292181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01383103,"threshold_uncertainty_score":0.02750105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06790108748205707,"score_gpt":0.2179760115151498,"score_spread":0.1500749240330927,"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."}}