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Record W2999748776 · doi:10.3390/w12010168

Performance and Kinetics of a Pond-Constructed Wetland System Treating Beef Manure Pile and Exercise Yard Runoff in Eastern Ontario

2020· article· en· W2999748776 on OpenAlexafffundabout
Juliana Mejia-Franco, Chris Kinsley, Robert Delatolla

Bibliographic record

VenueWater · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicConstructed Wetlands for Wastewater Treatment
Canadian institutionsUniversity of Ottawa
FundersMinistry of Agriculture, Food and Rural AffairsNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Agriculture, Food and Rural AffairsUniversity of Ottawa
KeywordsEnvironmental scienceSurface runoffManureWetlandPhosphorusNutrientPollutionNonpoint source pollutionBiochemical oxygen demandEnvironmental engineeringLivestockHydrology (agriculture)Water qualitySewage treatmentChemical oxygen demandEcologyChemistryBiology

Abstract

fetched live from OpenAlex

Runoff from manure piles and permanent outdoor confinement areas are common sources of pollution from livestock operations, which must be properly managed in accordance with local environmental regulations. Seven months of performance data from a pond-free water surface constructed wetland system receiving cattle feedlot runoff in Eastern Ontario, Canada, were used to evaluate performance and quantify first-order reaction rate constants (k) for organic matter and nutrients. Kinetic values were shown to be strongly temperature dependent and to adequately represent average treatment over the operating season; however, k values varied significantly on shorter time scales due to the event-based nature of the treatment system. Plant (duckweed) uptake of soluble phosphorus and ammonia in the pond was shown to be a predominant mechanism of nutrient removal. The pond-wetland system was effective at reducing biological oxygen demand (BOD5) by 67%, total phosphorus (TP) by 45%, and total nitrogen (TN) by 64%, providing a cost-effective and appropriate solution for beef producers.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.667

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 head0.007
GPT teacher head0.171
Teacher spread0.164 · 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

Citations6
Published2020
Admission routes3
Has abstractyes

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