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Record W2049064637 · doi:10.1080/10934520902847844

Oxygen release compound as a chemical treatment for nutrient rich estuary sediments and water

2009· article· en· W2049064637 on OpenAlexaff
Elsadig A.M. Abdallah, Alex Augusto Gonçalves, Graham A. Gagnon

Bibliographic record

VenueJournal of Environmental Science and Health Part A · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOrganic matterEnvironmental chemistrySedimentNutrientEnvironmental scienceAquatic toxicologyAcute toxicityEstuaryToxicityEnvironmental remediationChemistryContaminationFisheryEcologyBiology

Abstract

fetched live from OpenAlex

The objectives of this work were: (i) to evaluate the efficacy of Oxygen Release Compound (ORC), oxygen providing agent that enhance the oxidation of organic matter in fresh water, marine water, and sediment; and (ii) to explore the potential aquatic toxicity that might be generated due to its use. A bench scale laboratory experiments were conducted using five different water sources (2 freshwater, 2 marine water and a deionised water). During the assay, flasks of 1 L capacity were dosed with artificial sediment and ORC and kept at room temperature for 10 days. Temperature, pH, DO, UV254, TOC and Mg were periodically measured. The potential aquatic toxicity that might be generated as a by-product of ORC use was performed. The experimental results reveal that ORC is indeed effective in releasing oxygen over a long period of time and it is also effective for the remediation of natural waters enriched with organic matter. The toxicity test shows that ORC treatment did not create any biological toxicity in freshwater samples (IC50<1 Toxicity Unit-TU). However, marine water samples reveal a high toxicity and had IC50>1 TU. The study proves that ORC was an appropriate technology that can safely be used to treat natural waters enriched with nutrient and natural organic matter.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.360

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)0.0000.000

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 head0.019
GPT teacher head0.278
Teacher spread0.260 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations9
Published2009
Admission routes1
Has abstractyes

Explore more

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