{"id":"W2782020368","doi":"10.1002/cjce.23128","title":"Adsorption optimization of a biomass‐based fly ash for treating thermomechanical pulping (TMP) pressate using definitive screening design (DSD)","year":2018,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Coal and Its By-products","field":"Earth and Planetary Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Fly ash; Pulp and paper industry; Biomass (ecology); Pulp (tooth); Effluent; Adsorption; Lignin; Chemical oxygen demand; Waste management; Wastewater; Materials science; Chemistry; Environmental science; Composite material; Environmental engineering; Organic chemistry; Agronomy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005163255,0.0001048313,0.0001666292,0.0001259949,0.00009761852,0.00004393494,0.0001741747,0.00006071702,0.00007609665],"category_scores_gemma":[0.0004809281,0.00007870464,0.00006492547,0.0002275598,0.00005892961,0.0001439616,0.000003966535,0.0001230077,7.664731e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002228447,"about_ca_system_score_gemma":0.000234154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001931033,"about_ca_topic_score_gemma":0.0003022817,"domain_scores_codex":[0.9991904,0.00003286677,0.0002845921,0.00009968644,0.0001385205,0.0002538569],"domain_scores_gemma":[0.9991267,0.0002338201,0.0001758215,0.00007887017,0.0002108282,0.0001740053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008395782,0.000002957084,0.000232368,0.00003258326,0.00003674416,0.000003150145,0.0001568571,0.8379689,0.1579557,0.00002273138,0.00001188736,0.003492199],"study_design_scores_gemma":[0.0002364409,0.0001077344,0.0001550636,0.0001534613,0.00003786783,0.00002803626,0.00001134561,0.8089771,0.1901298,0.00006326655,0.00001499646,0.00008487466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4666384,0.0002045054,0.5326297,0.0001580547,0.0001523801,0.0001435123,0.00002112093,0.000008562928,0.00004381242],"genre_scores_gemma":[0.9392851,5.867424e-7,0.06038265,0.00002679132,0.0002867734,4.092784e-7,0.000008042476,0.000007568885,0.000002059222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4726467,"threshold_uncertainty_score":0.3209482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04495756530537944,"score_gpt":0.2095749914379514,"score_spread":0.164617426132572,"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."}}