{"id":"W2605291414","doi":"10.1007/s10924-017-1011-8","title":"Determination of In Situ Esterification Parameters of Citric Acid-Glycerol Based Polymers for Wood Impregnation","year":2017,"lang":"en","type":"article","venue":"Journal of environmental polymer degradation","topic":"Lignin and Wood Chemistry","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Natural Sciences and Engineering Research Council of Canada","funders":"Natural Sciences and Engineering Research Council of Canada; University of Bath; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Citric acid; Materials science; Polymer; Chemical engineering; Thermal stability; Glycerol; Catalysis; Fourier transform infrared spectroscopy; Thermogravimetric analysis; Glass transition; Solvent; Organic chemistry; Polymer chemistry; Composite material; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002436527,0.0005539675,0.0001853673,0.000285018,0.0001557201,0.0002897592,0.0003211103,0.0004186017,0.0009848403],"category_scores_gemma":[0.0003636223,0.0002330545,0.0002665704,0.0002940283,0.0001601223,0.0004495641,0.0001582772,0.0005733277,0.0003017281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001826649,"about_ca_system_score_gemma":0.0001222065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005496921,"about_ca_topic_score_gemma":0.001654248,"domain_scores_codex":[0.9997968,0.00002957987,0.00001365326,0.00004272602,0.00007026864,0.00004689815],"domain_scores_gemma":[0.9998173,0.00006129212,0.00004272388,0.00001745996,0.00004220109,0.00001904694],"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":[0.00003348262,0.00001039987,0.0000773932,0.00002940452,0.000002526274,0.00001480049,0.00001425709,0.00006088931,0.9983491,0.00001987633,0.000009823007,0.001378066],"study_design_scores_gemma":[4.196578e-7,0.00001878425,0.0002715227,9.695021e-7,0.000003067284,0.000007390225,0.000004083228,0.0001786844,0.9993786,0.000002962455,0.0001320302,0.000001323059],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9811725,0.001477149,0.01456237,0.00004860014,0.00002928422,0.00003374588,0.0003094596,0.00008688006,0.002279986],"genre_scores_gemma":[0.9893374,0.001010203,0.006837706,0.00002371214,0.000006161107,0.00002576832,0.0002513046,0.00002792036,0.002479729],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009848403,"threshold_uncertainty_score":0.003294647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01185591150768935,"score_gpt":0.2208559929918684,"score_spread":0.2090000814841791,"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."}}