{"id":"W2328353438","doi":"10.1021/sc400289z","title":"Heavy Metal Removal (Copper and Zinc) in Secondary Effluent from Wastewater Treatment Plants by Microalgae","year":2013,"lang":"en","type":"article","venue":"ACS Sustainable Chemistry & Engineering","topic":"Algal biology and biofuel production","field":"Energy","cited_by":144,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Canada Research Chairs","keywords":"Effluent; Chlorella vulgaris; Wastewater; Sewage treatment; Scenedesmus; Zinc; Chlorella; Biosorption; Chemistry; Secondary treatment; Environmental chemistry; Pulp and paper industry; Copper; Algae; Microorganism; Chlorophyceae; Botany; Biology; Environmental engineering; Environmental science; Chlorophyta; Adsorption; Bacteria","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":[0.0001353197,0.000581054,0.0008607674,0.0002362531,0.000414353,0.0005338971,0.0002691133,0.0003833959,0.0003768861],"category_scores_gemma":[0.0001631105,0.0002039696,0.0005711776,0.0003218164,0.0002118413,0.0001205917,0.0004785275,0.0004424177,0.0003362239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004515251,"about_ca_system_score_gemma":0.0003630757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004714058,"about_ca_topic_score_gemma":0.007211447,"domain_scores_codex":[0.9997457,0.00002669416,0.00001873228,0.00005228417,0.0001168912,0.00003970785],"domain_scores_gemma":[0.9999024,0.00001049732,0.00002325415,0.00000973228,0.00002983606,0.00002432967],"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":[0.00003430765,0.00001228564,0.0005519539,0.00003485177,0.0000040903,0.00003316113,0.00001333659,0.00005964474,0.9983112,0.000003866533,0.000008927444,0.0009325572],"study_design_scores_gemma":[0.000007943812,0.000334114,0.006302562,0.000005854658,0.00001856512,0.00008150157,0.00004538629,0.0004818423,0.9922408,0.00001114787,0.000466661,0.000003703037],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970686,0.0002614968,0.002138396,0.00002391034,0.000008318,0.00002433835,0.00007722535,0.00005047387,0.0003471383],"genre_scores_gemma":[0.993314,0.000456215,0.003610163,0.00003445723,0.000005151261,0.00002746018,0.0002658122,0.00001976721,0.002266875],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004714058,"threshold_uncertainty_score":0.009373248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003954993131970375,"score_gpt":0.1757970916757405,"score_spread":0.1718420985437701,"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."}}