{"id":"W4413265622","doi":"10.3390/app15158518","title":"Innovative Valorization of Wood Panel Waste into Activated Biochar for Efficient Phenol Adsorption","year":2025,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Adsorption and biosorption for pollutant removal","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Technologique des Résidus Industriels; Cégep de l'Abitibi Témiscamingue; Université du Québec en Abitibi-Témiscamingue","funders":"Fonds de recherche du Québec – Nature et technologies; Centre Technologique des Résidus Industriels","keywords":"Biochar; Adsorption; Pyrolysis; Langmuir adsorption model; Chemistry; Phenol; Aqueous solution; Wastewater; Pulp and paper industry; Nuclear chemistry; Environmental chemistry; Waste management; Environmental engineering; Environmental science; Organic 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":[0.0001166159,0.0004113514,0.0001790793,0.0002677581,0.0001188368,0.0002599028,0.000245964,0.0003050356,0.0006249514],"category_scores_gemma":[0.0001098599,0.0001331195,0.0003746156,0.0002762227,0.0001051866,0.0002755304,0.0002031409,0.0003341935,0.0003082851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001387489,"about_ca_system_score_gemma":0.0002156317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003901247,"about_ca_topic_score_gemma":0.001236906,"domain_scores_codex":[0.9998847,0.00001364413,0.00000894352,0.00002096647,0.00003852553,0.00003321812],"domain_scores_gemma":[0.999971,0.000003975294,0.000006843192,0.000004376511,0.000008900826,0.000004846653],"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":[0.00002698041,0.00002664793,0.0001060533,0.00007448103,0.000008574737,0.00004752623,0.000008720791,0.0003050837,0.9955702,0.0001183776,0.00003165742,0.003675681],"study_design_scores_gemma":[0.000004893607,0.0001061839,0.0007311078,0.000003535068,0.00001093591,0.00007283826,0.0000121757,0.001589889,0.9961092,0.00004627211,0.001307952,0.000005085519],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9775158,0.002246814,0.0169912,0.00009454693,0.00006376637,0.00005352095,0.0002233952,0.0001390242,0.002672039],"genre_scores_gemma":[0.9897405,0.00102666,0.007441361,0.00003224534,0.000008597548,0.00002228555,0.0002002257,0.00001782471,0.001510237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006249514,"threshold_uncertainty_score":0.002090693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01906368921255014,"score_gpt":0.2558895355168264,"score_spread":0.2368258463042763,"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."}}