{"id":"W2171256973","doi":"10.1155/2013/825318","title":"Qualitative and Quantitative Control of Honeys Using NMR Spectroscopy and Chemometrics","year":2013,"lang":"en","type":"article","venue":"ISRN Analytical Chemistry","topic":"Bee Products Chemical Analysis","field":"Agricultural and Biological Sciences","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Rural Affairs","keywords":"Chemometrics; Principal component analysis; Chemistry; Partial least squares regression; Context (archaeology); Nuclear magnetic resonance spectroscopy; Multivariate statistics; Spectroscopy; Analytical Chemistry (journal); Linear discriminant analysis; Monosaccharide; Chromatography; Fructose; Artificial intelligence; Mathematics; Food science; Computer science; Stereochemistry; Organic chemistry; Physics; Biology; Statistics","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":[0.001713821,0.0005824254,0.000495284,0.002126854,0.0003872217,0.001017905,0.0003319223,0.0006290795,0.000569155],"category_scores_gemma":[0.002423275,0.0002636912,0.0004265844,0.001314059,0.0009659726,0.0004890255,0.0003526431,0.0004422613,0.0002068617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003908832,"about_ca_system_score_gemma":0.0003949047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009278119,"about_ca_topic_score_gemma":0.001396831,"domain_scores_codex":[0.9977427,0.000495226,0.0001804218,0.0004594463,0.001044955,0.00007726502],"domain_scores_gemma":[0.9990582,0.0002545206,0.0002329321,0.0001052964,0.0003068292,0.00004223763],"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":[0.00008904958,0.00005332924,0.001398982,0.000136324,0.00001763475,0.00003135847,0.00006277017,0.0007322523,0.9691294,0.000236457,0.00005795418,0.02805442],"study_design_scores_gemma":[0.00002592324,0.0006446063,0.05456581,0.0000435393,0.00008227395,0.0003589657,0.0001773036,0.04414494,0.89525,0.0009581287,0.00364698,0.0001015248],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.757202,0.004690941,0.2308108,0.0002596832,0.0001616444,0.0004748924,0.001303126,0.0006018174,0.004495037],"genre_scores_gemma":[0.75937,0.001436412,0.2365675,0.0001488929,0.00008841541,0.0004429525,0.0005779728,0.00009335698,0.001274596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002126854,"threshold_uncertainty_score":0.009063721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04034982312269603,"score_gpt":0.3049663817765411,"score_spread":0.2646165586538451,"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."}}