{"id":"W2922543726","doi":"10.22215/etd/2018-12942","title":"Utilization of Ultraviolet-Visible Spectroscopy and Rheology for Sludge Characterization and Monitoring","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Rheology; Absorbance; Effluent; Aerobic digestion; Activated sludge; Ultraviolet visible spectroscopy; Sewage treatment; Chemistry; Ultraviolet; Wastewater; Pulp and paper industry; Characterization (materials science); Environmental science; Process engineering; Waste management; Materials science; Chromatography; Environmental engineering; Engineering; Nanotechnology; Composite material","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":[0.001619305,0.000947326,0.0006909473,0.001665727,0.000457622,0.001252476,0.0004701143,0.0008003153,0.001967303],"category_scores_gemma":[0.00145194,0.0003683708,0.0006207864,0.001221341,0.0005511884,0.001375859,0.0006299174,0.001088418,0.001660807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002489613,"about_ca_system_score_gemma":0.0006678483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003484825,"about_ca_topic_score_gemma":0.0008901593,"domain_scores_codex":[0.9987903,0.0002581421,0.00006672866,0.0003237938,0.0004794746,0.00008157051],"domain_scores_gemma":[0.9990746,0.0003203429,0.0001123731,0.0001577005,0.0002785425,0.00005630125],"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":[0.00009432311,0.0001634509,0.001449779,0.0007668865,0.00004509611,0.00003991619,0.00007511723,0.0002944365,0.897189,0.00056738,0.000502395,0.09881213],"study_design_scores_gemma":[0.000008879845,0.0003459942,0.00527267,0.00007361641,0.00005732103,0.0002030727,0.00008961761,0.001373002,0.9802833,0.0008060044,0.01143887,0.00004757318],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3977631,0.08268483,0.4552332,0.001322601,0.001430635,0.0004806296,0.001486173,0.001920802,0.05767798],"genre_scores_gemma":[0.7035154,0.05365132,0.2101144,0.0007171819,0.0004423105,0.0003220809,0.001459714,0.0004187608,0.02935883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001967303,"threshold_uncertainty_score":0.008563817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01890412510460693,"score_gpt":0.2830742970290464,"score_spread":0.2641701719244395,"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."}}