{"id":"W1984060324","doi":"10.1007/s11947-008-0162-y","title":"Volatiles Evaluation and Dielectric Properties Measurements of Chinese Spirits for Quality Assessment","year":2008,"lang":"en","type":"article","venue":"Food and Bioprocess Technology","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dielectric; Principal component analysis; Chromatography; Linear discriminant analysis; Gas chromatography; Spectrum analyzer; Network analyzer (electrical); Alcohol; Ethanol; Chemistry; Analytical Chemistry (journal); Materials science; Mathematics; Statistics; Computer science; Organic chemistry; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000690551,0.000124677,0.0002149425,0.0001431175,0.00005791088,0.00000377049,0.00009204884,0.0001785261,7.966407e-7],"category_scores_gemma":[0.0002258672,0.00009549451,0.00001541585,0.0002866261,0.0001862091,0.0000676226,0.00003541779,0.00009636806,9.468419e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002944409,"about_ca_system_score_gemma":0.000006860728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001395347,"about_ca_topic_score_gemma":0.000007230875,"domain_scores_codex":[0.9993334,0.000004078809,0.0002018428,0.0001716146,0.0001325743,0.0001564202],"domain_scores_gemma":[0.9996504,0.00001652944,0.00005351795,0.0001155667,0.0001483743,0.00001564481],"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":[0.00001265395,0.00002941569,0.02663937,0.0003615624,0.00004030148,1.235266e-7,0.00004285611,0.00004264226,0.9298195,0.0001513737,0.00000429341,0.04285594],"study_design_scores_gemma":[0.0004428998,0.0002820238,0.006059073,0.00002598574,0.00001480809,0.000009860884,0.00005970871,0.003881747,0.9770616,0.01196935,0.00003880644,0.0001541028],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933117,0.004912992,0.0007805601,0.0001306085,0.00002001144,0.0003298379,0.000006613976,0.0004278982,0.00007976999],"genre_scores_gemma":[0.9970552,0.0002202301,0.002573763,0.000003441319,0.0000083079,0.0001204067,0.000002967431,0.00001158449,0.000004060116],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04724217,"threshold_uncertainty_score":0.3894153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07521908575121167,"score_gpt":0.3122712211028261,"score_spread":0.2370521353516145,"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."}}