{"id":"W2421810572","doi":"10.1021/acs.biomac.6b00603","title":"Cellulose Aggregation under Hydrothermal Pretreatment Conditions","year":2016,"lang":"en","type":"article","venue":"Biomacromolecules","topic":"Advanced Cellulose Research Studies","field":"Materials Science","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Devon Energy (Canada); National Institute for Nanotechnology; Natural Resources Canada","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Cellulose; Depolymerization; Biomass (ecology); Lignocellulosic biomass; Chemistry; Self-healing hydrogels; Chemical engineering; Biopolymer; Solvation; Solvent; Organic chemistry; Polymer","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001149951,0.0001650415,0.0001406657,0.00007258992,0.0001782485,0.000039682,0.0001707141,0.00004866849,0.001153435],"category_scores_gemma":[0.00005853002,0.0001022472,0.00006377701,0.0001190508,0.0003437351,0.0001934872,0.000105857,0.00002490346,0.001556495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001587523,"about_ca_system_score_gemma":0.00004415082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002666497,"about_ca_topic_score_gemma":0.00001440946,"domain_scores_codex":[0.9986704,0.00009409678,0.0001840697,0.0003452771,0.0003073368,0.0003988091],"domain_scores_gemma":[0.999328,0.0001325489,0.00007619186,0.0002820462,0.00007082687,0.0001103958],"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":[0.00001608635,0.00007751787,0.0001011113,0.000008617044,0.00002006873,0.0000362902,0.0000501093,0.000003571652,0.9896186,0.006945098,0.0004125128,0.002710392],"study_design_scores_gemma":[0.0005173872,0.00009388648,0.001143653,0.00005258746,0.00001151188,0.00001265695,0.00003235487,0.00001054005,0.9908298,0.005712962,0.001419968,0.0001627455],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9755856,0.0006969867,0.01951395,0.0009037365,0.0001824294,0.0003252077,0.0001060613,0.0001875727,0.00249849],"genre_scores_gemma":[0.9955178,0.0000842732,0.001506196,0.00004680383,0.00006244887,0.0001060755,0.00001095021,0.0000226662,0.002642761],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01993226,"threshold_uncertainty_score":0.9997597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01891772515404089,"score_gpt":0.2892545845948662,"score_spread":0.2703368594408253,"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."}}