{"id":"W4285030258","doi":"10.3390/en15145071","title":"Pyrolysis of Chromated Copper Arsenate-Treated Wood: Investigation of Temperature, Granulometry, Biochar Yield, and Metal Pathways","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Technologique des Résidus Industriels; Université du Québec en Abitibi-Témiscamingue","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Biochar; Chromated copper arsenate; Pyrolysis; Leaching (pedology); Toxicity characteristic leaching procedure; Arsenic; Incineration; Charcoal; Chemistry; Waste management; Chromium; Environmental chemistry; Pulp and paper industry; Copper; Metallurgy; Materials science; Environmental science; Heavy metals","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":[0.0001748855,0.0002748524,0.0002035401,0.0004029663,0.0001733609,0.0002979283,0.0001370863,0.0001701042,0.0005320619],"category_scores_gemma":[0.0001818998,0.0001702176,0.0003047702,0.0004027368,0.0001950226,0.0002153166,0.0001215325,0.0002790358,0.0001409286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001849561,"about_ca_system_score_gemma":0.0001440144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001662552,"about_ca_topic_score_gemma":0.003115792,"domain_scores_codex":[0.9998748,0.00001274704,0.000009165818,0.00002809088,0.00005267472,0.00002245439],"domain_scores_gemma":[0.999917,0.00002216132,0.00001963118,0.000006718142,0.00002674507,0.000007763065],"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":[0.0001321804,0.00002647942,0.00212772,0.00005048881,0.00001228163,0.00005388476,0.00005039015,0.0003444071,0.9949564,0.00005530118,0.00002007607,0.002170355],"study_design_scores_gemma":[0.000003016068,0.0001019458,0.01649648,0.000004960738,0.00001839747,0.00007206577,0.00006824124,0.001658655,0.9812262,0.00002880046,0.0003142748,0.000006976289],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998049,0.0002103392,0.001120193,0.000006870569,0.000004217226,0.000007444957,0.0001726919,0.0000156861,0.0004136143],"genre_scores_gemma":[0.9978623,0.0002596817,0.001146344,0.000006752578,0.000001985638,0.000008885277,0.0001524067,0.00001523814,0.0005463538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001662552,"threshold_uncertainty_score":0.003305733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009846678134174799,"score_gpt":0.1798594822513872,"score_spread":0.1700128041172124,"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."}}