{"id":"W3012134579","doi":"","title":"Effects of chemical and biological membrane filtration pre-treatment processes on NOM characteristics","year":2019,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Aerosol Filtration and Electrostatic Precipitation","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Filtration (mathematics); Membrane; Water treatment; Chemistry; Environmental chemistry; Environmental science; Mathematics; Environmental engineering; Biochemistry; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002692866,0.0001695748,0.0003031234,0.00007475672,0.00003026738,0.000008672962,0.00007617346,0.0002113482,0.00000398235],"category_scores_gemma":[0.00004072154,0.0001858742,0.00004212981,0.00008355171,0.00003061955,0.0000814324,0.000005690952,0.00009713488,0.000008396325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007220725,"about_ca_system_score_gemma":0.00004152522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003943138,"about_ca_topic_score_gemma":0.00108472,"domain_scores_codex":[0.9994797,0.00002037147,0.0001047197,0.0001649318,0.0001231644,0.0001070843],"domain_scores_gemma":[0.9995067,0.0001500972,0.0001328038,0.00009254539,0.00008237187,0.00003554159],"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":[0.0009820972,0.0003722883,0.003776974,0.008877563,0.0003606446,0.00001139203,0.004412101,0.0003226135,0.9733272,0.0005137055,0.0004274526,0.006615983],"study_design_scores_gemma":[0.001704804,0.001471193,0.2704647,0.0007488832,0.0003295618,0.000002821114,0.003330032,0.00470455,0.7163543,0.00003877881,0.0002691483,0.0005813452],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982715,0.00009784946,0.000452684,0.0000249997,0.0001386587,0.0003871755,0.0000360626,0.00005777609,0.0005332572],"genre_scores_gemma":[0.9977955,0.0008095434,0.0003031581,0.000004263283,0.00002282006,0.000001610238,0.0008725907,0.00001447211,0.0001760601],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2666877,"threshold_uncertainty_score":0.7579732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007582126770735351,"score_gpt":0.1894481747505241,"score_spread":0.1818660479797888,"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."}}