{"id":"W54644102","doi":"10.1007/s00107-013-0766-4","title":"Multi-elemental analysis of wood waste using energy dispersive X-ray fluorescence (ED-XRF) analyzer","year":2013,"lang":"en","type":"article","venue":"European Journal of Wood and Wood Products","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Université Laval","keywords":"Raw material; Environmental science; Chromated copper arsenate; Elemental analysis; Hardwood; Waste management; Pulp and paper industry; Contamination; Materials science; Environmental chemistry; Metallurgy; Chromium; Chemistry; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001109248,0.000298943,0.0005538269,0.0002585448,0.0001410144,0.00005866814,0.0004948491,0.00003241964,0.0008879654],"category_scores_gemma":[0.000125962,0.0002381328,0.0002088767,0.0007944558,0.0003857178,0.0005770273,0.0003954865,0.0002376556,0.0000598213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001193496,"about_ca_system_score_gemma":0.00001867034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008871563,"about_ca_topic_score_gemma":0.000007307049,"domain_scores_codex":[0.9968702,0.000618905,0.0009001694,0.0004896076,0.0006982261,0.0004229338],"domain_scores_gemma":[0.9983832,0.0000401543,0.0007488272,0.0004662963,0.00005191315,0.0003096334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00004237362,0.0006524014,0.01750891,0.0000250849,0.001233791,0.0001132064,0.002038812,0.01364503,0.9449474,0.000006240736,0.001259677,0.01852706],"study_design_scores_gemma":[0.005888218,0.002766136,0.4696969,0.0004514398,0.006594707,0.0003034005,0.007379063,0.02316827,0.4691785,0.00003737552,0.01213468,0.002401267],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972208,0.0007321402,0.0009448996,0.0002853684,0.0001969167,0.000149637,0.00001183245,0.00000716342,0.0004512037],"genre_scores_gemma":[0.9776978,0.0003247484,0.02140167,0.0001155622,0.0001733039,0.000001038922,0.000004464911,0.00003688986,0.0002444811],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4757689,"threshold_uncertainty_score":0.9722599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01492656039803863,"score_gpt":0.2194997697410097,"score_spread":0.204573209342971,"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."}}