{"id":"W7117586133","doi":"10.18280/ijdne.201119","title":"NIR and Machine Learning-Based Rapid Monitoring of pH and Moisture in Citronella Residue Fermentation","year":2025,"lang":"","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Fungal Biology and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Residue (chemistry); Fermentation; Moisture; Water content","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":[0.0006430268,0.0001832778,0.000372794,0.0005475867,0.00007653046,0.00003631274,0.0001423075,0.0004025018,0.00001063945],"category_scores_gemma":[0.0002737504,0.0001642111,0.00005907752,0.0001933214,0.0001542764,0.0001152931,0.0000489936,0.001299777,2.260828e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001046675,"about_ca_system_score_gemma":0.0001733696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002343173,"about_ca_topic_score_gemma":0.00001966489,"domain_scores_codex":[0.9986579,0.0001430856,0.0006313861,0.0002089464,0.0002190822,0.0001396456],"domain_scores_gemma":[0.9985623,0.0004630964,0.0004645927,0.00007444887,0.0003582409,0.00007731114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004787836,0.0004370082,0.8808204,0.0003119633,0.0009230023,0.0001166079,0.0007026255,0.004415134,0.07121713,0.002093251,0.0001277606,0.03404728],"study_design_scores_gemma":[0.007858047,0.001146173,0.920032,0.002381573,0.0004758869,0.0002504739,0.0008030095,0.04616684,0.01586578,0.004201448,0.0005420144,0.000276734],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9486302,0.03690165,0.008344158,0.004893999,0.0007606096,0.000274093,0.00003486303,0.000005045588,0.0001554049],"genre_scores_gemma":[0.9845384,0.01259253,0.002251314,0.0002205997,0.0001798521,0.000003297688,0.00002498284,0.000009722635,0.00017928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05535134,"threshold_uncertainty_score":0.6696334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01076472294194113,"score_gpt":0.2851024005052357,"score_spread":0.2743376775632946,"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."}}