{"id":"W2998203400","doi":"10.1016/j.jece.2019.103648","title":"Multivariate data analysis of full-scale sludge dewatering","year":2019,"lang":"en","type":"article","venue":"Journal of environmental chemical engineering","topic":"Coagulation and Flocculation Studies","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dewatering; Activated sludge; Pulp and paper industry; Waste management; Environmental science; Volume (thermodynamics); Mixed liquor suspended solids; Sewage sludge; Sewage treatment; Environmental engineering; Engineering; Geotechnical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001826421,0.0001065035,0.0002930863,0.00008645684,0.00001353185,0.000007401341,0.0002678028,0.00004130217,0.00188449],"category_scores_gemma":[0.00001854462,0.00009468184,0.0001216027,0.0002161574,0.00003717931,0.0002456525,0.0004103802,0.0001100075,0.00004006172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001070897,"about_ca_system_score_gemma":0.000001575587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000639409,"about_ca_topic_score_gemma":7.469078e-7,"domain_scores_codex":[0.9989556,0.0000080728,0.0004211651,0.0001590432,0.0003277409,0.000128344],"domain_scores_gemma":[0.9994125,0.00004807293,0.0001876976,0.0002742649,0.000002450497,0.00007502798],"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":[0.00001718055,0.00004037986,0.05383253,0.000004718689,0.0001752593,0.000001234832,0.00009992326,0.1262708,0.8190776,0.00000136587,0.00001564199,0.0004633983],"study_design_scores_gemma":[0.000587161,0.00003821379,0.7173953,0.00001884605,0.000399831,0.00001795077,0.00007305307,0.244762,0.03492312,0.000004420219,0.001566347,0.0002137768],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943877,0.00004618854,0.005220082,0.00002101792,0.00008025127,0.00004545839,0.00002266989,0.000006408465,0.0001701734],"genre_scores_gemma":[0.9929835,0.00001140135,0.006901884,0.00001322694,0.00002395961,4.040372e-7,0.00001507275,0.000009559953,0.00004095141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7841544,"threshold_uncertainty_score":0.9990279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01183903971639852,"score_gpt":0.2200910782392967,"score_spread":0.2082520385228982,"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."}}