MétaCan
Menu
Back to cohort

Advanced Treatment Process for Pharmaceuticals, Endocrine Disruptors, and Flame Retardants Removal

2014· article· en· W2411900660 on OpenAlexaff
Robert W. Emerick, Stanley E. Shumaker

Bibliographic record

VenueWater Environment Research · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsStantec (Canada)
FundersUniversity of Arizona
KeywordsReverse osmosisEffluentChemistryFiltration (mathematics)Water treatmentWaste managementNanofiltrationPowdered activated carbon treatmentActivated carbonPulp and paper industryEnvironmental scienceEnvironmental chemistryEnvironmental engineeringMembraneOrganic chemistryAdsorption

Abstract

fetched live from OpenAlex

The objective of this project was to demonstrate the effectiveness of an advanced treatment process that did not utilize reverse osmosis for the removal of pharmaceuticals, endocrine disruptors and flame retardants (collectively referred as contaminants of emerging concern [CECs]) from municipal effluent. The advanced treatment process consisted of (in the order of use): membrane filtration, ozonation (O3), and biologically active carbon (BAC) filtration. Ozone dosage of 5 mg/L or more was needed for desired CEC removal. Biologically active carbon removed flame retardants, and ozonation byproducts including NDMA and aldehydes. The project successfully demonstrated 1) the removal of a wide range of CECs, 2) reduction of estrogen activity to background levels, and 3) removal of ozonation byproducts. Treatment was achieved at lower costs and power utilization than reverse osmosis and without generating a concentrate stream. Results from this project could make CEC removal feasible, especially in situations where reverse osmosis treatment is infeasible.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.086
GPT teacher head0.404
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations24
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueWater Environment ResearchSame topicPharmaceutical and Antibiotic Environmental ImpactsFrench-language works237,207