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Record W1990336735 · doi:10.4141/cjas08055

Monthly NH<sub>3</sub> emissions from poultry in 12 Ecoregions of Canada

2009· article· en· W1990336735 on OpenAlexvenueaboutno aff
Steve Sheppard, Shabtai Bittman

Bibliographic record

VenueCanadian Journal of Animal Science · 2009
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceManureEutrophicationManure managementAmmoniaBarnNutrientEnvironmental protectionAtmospheric sciencesAgronomyEcologyGeographyBiology

Abstract

fetched live from OpenAlex

Management of ammonia (NH 3 ) is a multi faceted issue for farmers. It is simultaneously a toxicant that can affect farm worker and animal health, a volatile plant nutrient that is expensive to replace if lost, and a potential contributor to environmental degradation. The environmental implications have important spatial and temporal dimensions, beyond the farm. This paper describes a model developed to estimate NH 3 emissions from poultry (broiler, layer and turkey) production in 2780 mapping units across Canada on a monthly time scale. It includes estimates of daily emission peaks within critical months. The results will contribute to estimates of haze and atmospheric aerosol production, as well as contributions to other potential impacts such as eutrophication of sensitive ecosystems. The model is based on a detailed survey of farm practices. Emissions vary strongly throughout the year, and in many regions there are peak emissions in early spring and late fall, associated with landspreading of manure. There are also markedly different nitrogen excretion rates among regions, and these and bird populations are the key factors controlling emissions. On average, 22% of excreted uric acid or ammoniacal N is emitted from barns, 2% from storage and 26% from landspreading, resulting in a total loss of 50%. Key words: Ammonia, PM 2.5 , acid rain, nitrogen

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.789

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 head0.011
GPT teacher head0.206
Teacher spread0.194 · 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

Citations20
Published2009
Admission routes2
Has abstractyes

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