{"id":"W3118408107","doi":"10.3390/fermentation7010013","title":"Predicting Fermentation Rates in Ale, Lager and Whisky","year":2021,"lang":"en","type":"article","venue":"Fermentation","topic":"Fermentation and Sensory Analysis","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Memorial University of Newfoundland","funders":"Dalhousie University; Scottish Funding Council","keywords":"Akaike information criterion; Sigmoid function; Logistic function; Goodness of fit; Statistics; Mathematics; Econometrics; Logistic regression; Applied mathematics; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001137786,0.00007609571,0.00009232004,0.00001974608,0.000118279,0.00008121121,0.00003109197,0.00003856522,0.0007703989],"category_scores_gemma":[0.00002962974,0.00003948919,0.00002858086,0.0003438469,0.00001804685,0.0002388629,0.00001848374,0.00005055479,0.00001969257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003330449,"about_ca_system_score_gemma":0.000005249305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001304253,"about_ca_topic_score_gemma":0.001110315,"domain_scores_codex":[0.9992186,0.0001148044,0.0001960842,0.0002159916,0.0001327527,0.0001218004],"domain_scores_gemma":[0.9997858,0.00004719432,0.00005844014,0.00002399767,0.00004391798,0.00004060155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001270963,0.00004866742,0.3260241,0.00000530225,0.000008728525,0.000007706515,0.0005685731,0.00002852058,0.657214,0.0001007589,0.00005968516,0.01592128],"study_design_scores_gemma":[0.0002740321,0.00003527609,0.967679,0.00001246503,0.0000134418,0.000005221587,0.004833359,0.0008752886,0.0257278,0.0003321243,0.0001245691,0.00008744495],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997914,0.00005456073,0.000004500322,0.0009975786,0.00005756401,0.00009966513,0.000009203879,0.00002557338,0.0008374201],"genre_scores_gemma":[0.9976086,0.00009599104,0.0001176678,0.0005283419,0.00004920849,0.00001580964,0.0004164648,7.057099e-7,0.001167209],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6416548,"threshold_uncertainty_score":0.8435328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0203111341962077,"score_gpt":0.2523212782903792,"score_spread":0.2320101440941715,"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."}}