{"id":"W4312132030","doi":"10.1186/s13068-022-02245-4","title":"Evaluation of engineered low-lignin poplar for conversion into advanced bioproducts","year":2022,"lang":"en","type":"article","venue":"Biotechnology for Biofuels and Bioproducts","topic":"Lignin and Wood Chemistry","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Lawrence Berkeley National Laboratory; Great Lakes Bioenergy Research Center; Biological and Environmental Research; National Nuclear Security Administration; Office of Science; Sandia National Laboratories; U.S. Department of Energy","keywords":"Bioproducts; Lignin; Biomass (ecology); Lignocellulosic biomass; Bioenergy; Chemistry; Xylose; Cellulase; Biorefinery; Hydrolysate; Pulp and paper industry; Biofuel; Hydrolysis; Fermentation; Food science; Biotechnology; Biochemistry; Organic chemistry; Agronomy; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006991687,0.0002014646,0.0002593982,0.0001797795,0.0001577024,0.000007568725,0.000205569,0.0002346222,0.000009702791],"category_scores_gemma":[0.0001509863,0.0002056648,0.00006387132,0.0003414907,0.0001140347,0.00005584461,0.00009722287,0.00014032,0.000001491246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009681215,"about_ca_system_score_gemma":0.00004885333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003081723,"about_ca_topic_score_gemma":7.173615e-7,"domain_scores_codex":[0.9987516,0.00001287242,0.0002680887,0.0004475027,0.0002377695,0.0002821496],"domain_scores_gemma":[0.9992964,0.00002275666,0.0000777327,0.0003718123,0.0001888024,0.00004249885],"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":[0.00002537391,0.00004869851,0.00001141935,0.0006041108,0.00007258388,3.431613e-7,0.00007011285,0.0001614944,0.9790603,0.0002908066,0.00184754,0.01780721],"study_design_scores_gemma":[0.001435252,0.0002805519,0.000006435423,0.00001831578,0.0001027686,0.00001058649,0.0002534162,0.002487719,0.9795288,0.001891477,0.01373724,0.0002474214],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906121,0.005724179,0.0001048139,0.001126062,0.0007571506,0.001111344,0.0002513846,0.0002883617,0.00002460481],"genre_scores_gemma":[0.9953856,0.0002019529,0.003701629,0.00001931936,0.0001001435,0.0002685226,0.0002235327,0.00003598149,0.00006330907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01755979,"threshold_uncertainty_score":0.8386768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01062348523271852,"score_gpt":0.2229343812942904,"score_spread":0.2123108960615719,"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."}}