{"id":"W2093183583","doi":"10.1016/j.biortech.2012.01.089","title":"Quantitative characterization of lignocellulosic biomass using surrogate mixtures and multivariate techniques","year":2012,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Partial least squares regression; Principal component regression; Calibration; Multivariate statistics; Chemometrics; Cellulose; Lignocellulosic biomass; Regression analysis; Linear regression; Biological system; Mathematics; Chemistry; Analytical Chemistry (journal); Statistics; Chromatography; Organic chemistry","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":[0.00009622455,0.0001667391,0.0002876902,0.0005085379,0.00007087251,0.000009521676,0.0001578599,0.0003441089,0.0000725287],"category_scores_gemma":[0.0001058053,0.0001529433,0.00004393686,0.0007488337,0.0003150691,0.00008294907,0.0001138622,0.0001344301,0.000002724105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003511971,"about_ca_system_score_gemma":0.00001078532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006880738,"about_ca_topic_score_gemma":7.379184e-7,"domain_scores_codex":[0.9991609,0.00001423111,0.0002425051,0.0002043509,0.00009216945,0.0002858488],"domain_scores_gemma":[0.9993613,0.00004928455,0.0002542911,0.000226022,0.00006003622,0.00004900236],"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":[0.00001959634,0.00007680597,0.07916187,0.00006434553,0.00006122811,9.795723e-7,0.0001323651,3.8133e-8,0.9189027,0.0009194426,0.000002479112,0.0006581299],"study_design_scores_gemma":[0.0001410096,0.00004019601,0.002279618,0.00002664045,0.0001097111,0.00001348101,0.0002917794,0.0001425931,0.9962412,0.0001244928,0.0004336067,0.0001557419],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958075,0.001302185,0.00213387,0.00008514274,0.00001731892,0.00005725679,0.00002872863,0.0002701002,0.0002978431],"genre_scores_gemma":[0.9957173,0.00005215235,0.004070623,0.0000118033,0.00004024578,0.000008727853,0.00001836695,0.00002214316,0.00005862048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0773384,"threshold_uncertainty_score":0.6236848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02615989848286853,"score_gpt":0.2954329998016348,"score_spread":0.2692731013187663,"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."}}