{"id":"W2050767877","doi":"10.1021/es703114r","title":"Optimization of a Biosorption Column Performance","year":2008,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Adsorption and biosorption for pollutant removal","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Biosorption; Sorption; Mass transfer; Chemistry; Desorption; Adsorption; Chromatography; Sorbent; Column (typography); Analytical Chemistry (journal); Yield (engineering); Simulated moving bed; Pressure drop; Materials science; Mechanics; Mathematics; Metallurgy; Geometry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006500302,0.0009649037,0.0007720204,0.0004570129,0.0004406444,0.001039547,0.0006306094,0.0007452751,0.0009518338],"category_scores_gemma":[0.0007050037,0.0002981946,0.0003671781,0.0004507925,0.0003103216,0.0005495297,0.0004836697,0.0004426641,0.000784786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009222848,"about_ca_system_score_gemma":0.0009542084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003256544,"about_ca_topic_score_gemma":0.00377433,"domain_scores_codex":[0.9995525,0.00006739666,0.00003303802,0.0001196531,0.0001408642,0.00008649686],"domain_scores_gemma":[0.9997128,0.00009215802,0.00003536302,0.00001955429,0.0001011104,0.00003905217],"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":[0.0001960811,0.00009565266,0.0008827606,0.00009822179,0.00001774351,0.00002461819,0.00002531879,0.003560753,0.9873468,0.00006334863,0.00006933833,0.007619313],"study_design_scores_gemma":[0.00002589593,0.0006694903,0.004558177,0.000009600602,0.00004149016,0.0000570369,0.00003832747,0.01312509,0.9798416,0.0000480237,0.001568143,0.00001714088],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9746909,0.0008968288,0.02161974,0.0001880361,0.00002659553,0.0001652224,0.0003211561,0.0004299692,0.001661569],"genre_scores_gemma":[0.9700458,0.0008373261,0.02637055,0.00009810666,0.000010467,0.0001423601,0.0004335826,0.00006653502,0.001995068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003256544,"threshold_uncertainty_score":0.006691635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006748928450340862,"score_gpt":0.1888590779452997,"score_spread":0.1821101494949588,"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."}}