MétaCan
Menu
Back to cohort
Record W1990424434 · doi:10.4141/cjas08034

Methane and ammonia emissions from a beef feedlot in western Canada for a twelve-day period in the fall

2008· article· en· W1990424434 on OpenAlexaffvenueabout
Ronald P. van Haarlem, R. L. Desjardins, Zhiling Gao, Thomas K. Flesch, Xuanyi Li

Bibliographic record

VenueCanadian Journal of Animal Science · 2008
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversity of AlbertaAgriculture and Agri-Food Canada
Fundersnot available
KeywordsFeedlotEnvironmental scienceBeef cattleMethaneAmmoniaMethane emissionsAnimal sciencePollutantParticulatesLivestockEnvironmental chemistryChemistryEcologyBiology

Abstract

fetched live from OpenAlex

Commercial feedlot operations are becoming a mainstay in the Canadian beef industry. These large operations that typically raise thousands of animals at a time represent a large localized source of methane (CH 4 ) and of atmospheric pollutants such as ammonia (NH 3 ) and particulate matter. An inverse dispersion model was utilized to calculate CH 4 and NH 3 emissions from a commercial cattle feedlot and an adjacent runoff retention pond. The feedlot measurements were collected within the interior of the feedlot enabling a near continuous emissions record over the 12 d of the study period. Average daily emission estimates of CH 4 and NH 3 were 323 and 318 g animal -1 d -1 , respectively. The CH 4 emissions represent 4% of the gross energy intake (GEI) and NH 3 emissions represent 72% of the total N intake. Emissions from the runoff retention pond associated directly with the feedlot operation were approximately 2.7 and 2% of the daily average feedlot emissions of CH 4 and NH 3 , respectively. Key words: Methane, ammonia, inverse dispersion, ruminant livestock, trace gas emissions

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.239
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations83
Published2008
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Animal ScienceSame topicOdor and Emission Control TechnologiesFrench-language works237,207