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Record W2003285326 · doi:10.4141/cjss08002

Ammonia emission from dairy cow manure stored in a lagoon over summer

2008· article· en· W2003285326 on OpenAlexfundvenueno aff
S. M. McGinn, Trevor Coates, Thomas K. Flesch, B. P. Crenna

Bibliographic record

VenueCanadian Journal of Soil Science · 2008
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsnot available
FundersAgriculture and Agri-Food Canada
KeywordsManureEnvironmental scienceAmmoniaAmmoniacal nitrogenLivestockFertilizerPollutionWater qualityDairy cattleSewageHydrology (agriculture)Environmental engineeringAnimal scienceWastewaterChemistryAgronomyEcologyBiology

Abstract

fetched live from OpenAlex

It is recognized that volatilized ammonia (NH 3 ) from intensive livestock production can be a significant pathway for nitrogen (N) pollution to land and water, and can contribute to poor air quality. The objectives of our study were to document NH 3 emissions from a dairy lagoon and to assess the influence of meteorology on NH 3 emissions. Ammonia emissions were determined using a backward Lagrangian Stochastic approach using WindTrax software, an open-path NH 3 laser and a sonic anemometer. Results indicate that an average 5.1 ± 1.6 g NH 3 m -2 d -1 was released over the summer; however, the emission varied typically over 24 h between 3.6 and 8.6 g NH 3 m -2 d -1 . Wind speed and surface temperature of the lagoon had similar influences on the magnitude of the release, where their direct impact on NH 3 emission accounted for 28 and 31% of the variability, respectively. The main implication of this study is that NH 3 losses are significant from dairy lagoons, contributing to the issue of N pollution. As well, NH 3 emissions are a loss of valuable N for manure used as fertilizer, which in our study amounted to approximately 13% of the total ammoniacal N content of the manure in the lagoon. Key words: Ammonia, dairy, manure, cattle, dispersion model

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)0.001
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 score0.356
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.232
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.

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

Citations42
Published2008
Admission routes2
Has abstractyes

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