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Evaluation of Electric Arc Furnace Slag as a Potential Phosphate-Removal Substrate

2009· article· en· W2024759304 on OpenAlexaff
Harjinder Kumar, Shiv O. Prasher, Ramanbhai M. Patel, Syed A. Hussain

Bibliographic record

VenueJournal of Environmental Engineering · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhosphorusAerationElectric arc furnaceSlag (welding)ChemistrySubstrate (aquarium)PhosphateWastewaterEnvironmental engineeringAdsorptionWaste managementPulp and paper industryEnvironmental chemistryEnvironmental scienceMetallurgyMaterials scienceGeology

Abstract

fetched live from OpenAlex

A study was conducted to evaluate the performance of locally available electric arc furnace slag (EAFS) as a substrate for removing phosphorus from wastewater. First, in a laboratory study, EAFS was found to have high phosphorus removal efficiency for three P concentrations (0.3, 3.0, and 6.0 mg/L); this resulted in nearly 100% phosphorus removal in 24 h. Next, the experiment was repeated using aeration and similar phosphorus removal was observed but in a shorter contact time of 1 h. The adsorption capacity of EAFS was determined to be 1,458 mg/kg. In a pilot-scale study, over 90% P removal took place in the first 4 h under nonaerated conditions, and nearly 100% removal in 8 h. While the P removal with aeration was relatively less initially for the shorter residence times, a 100% removal was observed for the 24-h residence time.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.005
GPT teacher head0.195
Teacher spread0.190 · 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

Citations6
Published2009
Admission routes1
Has abstractyes

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