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Record W2767367353 · doi:10.1021/acs.est.7b02353

Removal of Soluble Phosphorus from Surface Water Using Iron (Fe–Fe) and Aluminum (Al–Al) Electrodes

2017· article· en· W2767367353 on OpenAlexafffundabout
Tanner R. Devlin, Alessandro di Biase, V. Wei, Maria Elektorowicz, Jan A. Oleszkiewicz

Bibliographic record

VenueEnvironmental Science & Technology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsConcordia UniversityUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsAluminiumPhosphorusElectrodeMetallurgyChemistryEnvironmental chemistryMaterials scienceInorganic chemistry

Abstract

fetched live from OpenAlex

The removal of soluble phosphorus using iron and aluminum electrodes was studied in water samples from the Red River, a hyper-eutrophic stream in Winnipeg, Canada. Four trials were conducted: (I) mixed batch with 150–900 mA applied for 1 min to 1 L, (II) stagnant batch with 600–900 mA applied for 1 min to 1 L, and (III and IV) continuously stirred-tank reactor with 6.25–10 min hydraulic retention times and constant 900 mA. Maximum soluble phosphorus removals of 70–80% were observed in mixed batch, and there was no significant difference between aluminum and iron electrodes ( P value of 0.0526–0.9487). Aluminum electrodes performed significantly worse than iron electrodes under higher hydraulic loads, with iron removing >70% soluble phosphorus and aluminum <40% ( P values of 0.0035–0.0143). The estimated cost of consumables, reported per million liters of water treated, to remove 70% soluble phosphorus from eutrophic waters with 0.35 g m –3 soluble phosphorus would include 5–17.5 USD electricity costs and material costs of 5.3–12.2 USD for iron and 39.2 USD for aluminum.

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 categoriesScience and technology studies
Consensus categoriesnone
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 score0.043
Threshold uncertainty score0.997

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 studies0.0010.006
Scholarly communication0.0000.001
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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; a candidate call from one teacher head, not a consensus.

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

Citations35
Published2017
Admission routes3
Has abstractyes

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