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Record W3026457239

Potential Approach for the Adsorption of Phosphate from Agricultural Runoff using Plaster of Paris Powder and Hydrogel Beads

2020· dissertation· en· W3026457239 on OpenAlexfundno aff
Srdjan Malicevic

Bibliographic record

VenueThe Atrium (University of Guelph) · 2020
Typedissertation
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsnot available
FundersMitacsOntario Ministry of Agriculture, Food and Rural AffairsUniversity of Guelph
KeywordsAdsorptionPhosphateSurface runoffAgricultureChemical engineeringMaterials scienceChemistryEngineeringGeographyArchaeologyOrganic chemistryBiologyEcology
DOInot available

Abstract

fetched live from OpenAlex

Phosphorus released in lakes due to agricultural runoff causes eutrophication, deteriorating water quality and ecosystem harm. Adsorbing and recovering phosphorus could potentially contribute to a circular economy and reduce eutrophication. A literature review of phosphorus adsorbents was conducted to isolate for ideal adsorbents after defining criteria for surface water adsorption. Two adsorbents were studied for the removal of phosphate from water: plaster of Paris powder and hydrogel beads produced using alginate, carboxymethylcellulose, and aluminum. The reaction kinetics, adsorption capacity, and ability to desorb were compared. In deionised water, hydrogel beads had a maximum sorption capacity of 90.5 milligram phosphate per gram dry bead with an equilibration time of approximately 24 hours. In deionised water, plaster of Paris (POP) powder has a maximum capacity of 1.52 milligram phosphate per gram of powder with an equilibrium time of less than 10 minutes. Sorbents can potentially be reused following phosphate desorption, and desorbed phosphate may be reused as fertilizer.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.011
GPT teacher head0.189
Teacher spread0.178 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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