{"id":"W4409289479","doi":"10.5539/jas.v17n5p55","title":"Optimizing Vetiver Oil Yield and Quality: A Comprehensive Approach Integrating Traditional and Modern Extraction Techniques","year":2025,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Magnetic and Electromagnetic Effects","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Texas State University; U.S. Department of Agriculture","keywords":"Yield (engineering); Extraction (chemistry); Quality (philosophy); Computer science; Process engineering; Biochemical engineering; Environmental science; Engineering; Chemistry; Chromatography; Materials science","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":[0.0002674846,0.00007805946,0.0001074994,0.00004601332,0.0001359249,0.00006367831,0.00009148722,0.00004981379,0.000001481165],"category_scores_gemma":[0.0001133225,0.00004812804,0.00003032457,0.0001297464,0.0002033508,0.00002408507,0.00003450741,0.0001245425,3.695617e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001537436,"about_ca_system_score_gemma":0.00004620101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009155902,"about_ca_topic_score_gemma":0.000002151847,"domain_scores_codex":[0.9993913,0.00002946917,0.0001630378,0.0001503035,0.0001511603,0.0001147166],"domain_scores_gemma":[0.9995536,0.00004079278,0.0001168372,0.00004051684,0.0001927134,0.00005559438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001984993,0.00002311522,0.0000662369,0.0000177123,0.000007606473,5.440069e-7,0.0001168064,0.00001149696,0.9785731,0.0002968006,0.0001448387,0.0207219],"study_design_scores_gemma":[0.0007090883,0.00225443,0.1952923,0.0002805932,0.00007140172,0.001237188,0.002686064,0.000574071,0.7951146,0.00043357,0.0009606208,0.0003859851],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990312,0.001238427,0.003673016,0.0002749786,0.00004430937,0.00004099424,9.620173e-7,0.000003401672,0.004411901],"genre_scores_gemma":[0.9823467,0.0003173415,0.01689569,0.0001244561,0.00007242159,0.000002510141,0.000001725506,0.000001284753,0.0002378618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1952261,"threshold_uncertainty_score":0.1962605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02202718857075135,"score_gpt":0.274599529153479,"score_spread":0.2525723405827277,"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."}}