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Record W4205110364 · doi:10.3389/fanim.2021.795200

Current Perspectives on Achieving Pronounced Enteric Methane Mitigation From Ruminant Production

2022· article· en· W4205110364 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Animal Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgencia Nacional de Investigación y Desarrollo
KeywordsMethanogenesisGreenhouse gasRuminantRumenEnvironmental scienceProduction (economics)ProductivityMethaneBiotechnologyNatural resource economicsAgronomyFermentationBiologyFood scienceEcologyCropEconomics

Abstract

fetched live from OpenAlex

Limiting global warming to 1.5°C above pre-industrial levels by 2050 requires achieving net zero emissions of greenhouse gases by 2050 and a strong decrease in methane (CH 4 ) emissions. Our aim was to connect the global need for mitigation of the emissions of greenhouse gases and enteric CH 4 from ruminant production to basic research on the biological consequences of inhibiting rumen methanogenesis in order to better design strategies for pronounced mitigation of enteric CH 4 production without negative impacts on animal productivity or economic returns. Ruminant production worldwide has the challenge of decreasing its emissions of greenhouse gases while increasing the production of meat and milk to meet consumers demand. Production intensification decreases the emissions of greenhouse gases per unit of product, and in some instances has decreased total emissions, but in other instances has resulted in increased total emissions of greenhouse gases. We propose that decreasing total emission of greenhouse gases from ruminants in the next decades while simultaneously increasing meat and milk production will require strong inhibition of rumen methanogenesis. An aggressive approach to pronounced inhibition of enteric CH 4 emissions is technically possible through the use of chemical compounds and/or bromoform-containing algae, but aspects such as safety, availability, government approval, consumer acceptance, and impacts on productivity and economic returns must be satisfactorily addressed. Feeding these additives will increase the cost of ruminant diets, which can discourage their adoption. On the other hand, inhibiting rumen methanogenesis potentially saves energy for the host animal and causes profound changes in rumen fermentation and post-absorptive metabolism. Understanding the biological consequences of methanogenesis inhibition could allow designing strategies to optimize the intervention. We conducted meta-regressions using published studies with at least one treatment with >50% inhibition of CH 4 production to elucidate the responses of key rumen metabolites and animal variables to methanogenesis inhibition, and understand possible consequences on post-absorptive metabolism. We propose possible avenues, attainable through the understanding of biological consequences of the methanogenesis inhibition intervention, to increase animal productivity or decrease feed costs when inhibiting methanogenesis.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.017
GPT teacher head0.247
Teacher spread0.230 · 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