{"id":"W3129588566","doi":"10.1039/d0gc04307a","title":"Impact of temperature and <i>in situ</i> FeCo catalysis on the architecture and Young's modulus of model wood-based biocarbon","year":2021,"lang":"en","type":"article","venue":"Green Chemistry","topic":"Lignin and Wood Chemistry","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Discovery Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Ontario Ministry of Agriculture, Food and Rural Affairs; Ontario Research Foundation; University of Guelph","keywords":"Porosity; Maple; In situ; Modulus; Pyrolysis; Catalysis; Architecture; Materials science; Young's modulus; Composite material; Chemical engineering; Chemistry; Organic chemistry; Ecology; Engineering; Biology; Geography","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":[0.0001911993,0.0001848459,0.0001116878,0.00008559085,0.0001110595,0.0002306272,0.0001757856,0.0001627203,0.001428989],"category_scores_gemma":[0.0003579572,0.0001309167,0.00008841541,0.0001273688,0.0002267701,0.0002480193,0.00009001021,0.0002366662,0.0002190112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001861659,"about_ca_system_score_gemma":0.0001546044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009022279,"about_ca_topic_score_gemma":0.002146055,"domain_scores_codex":[0.9999156,0.00001205992,0.000006868953,0.00002156328,0.00002162064,0.00002229339],"domain_scores_gemma":[0.9998372,0.00008904479,0.00003200654,0.00001128512,0.00001891373,0.0000114746],"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":[0.000559604,0.00003695372,0.0004858937,0.00007038547,0.00001219268,0.00005981443,0.00005279965,0.0007629952,0.995819,0.0001551871,0.00008313716,0.001902007],"study_design_scores_gemma":[0.000004258881,0.0001280482,0.001748252,0.000003922829,0.00000645837,0.00001972877,0.00002214914,0.0006645285,0.9969879,0.00002701517,0.0003830406,0.000004800826],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974409,0.0003609473,0.0003309791,0.00002606962,0.00001233085,0.000006433384,0.0002483862,0.00001442678,0.001559397],"genre_scores_gemma":[0.9989327,0.0002284933,0.0002249245,0.00001196221,0.000001765623,0.000004569729,0.0001315983,0.000009000498,0.0004549728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001428989,"threshold_uncertainty_score":0.004780471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004933048512421702,"score_gpt":0.1924592129485934,"score_spread":0.1875261644361717,"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."}}