MétaCan
Menu
Back to cohort
Record W1987760510 · doi:10.1155/2010/945785

Methanogens: Methane Producers of the Rumen and Mitigation Strategies

2010· review· en· W1987760510 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueArchaea · 2010
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMethanogenMethanogenesisRumenDefaunationBiologyPopulationMicroorganismMethaneMonensinArchaeaAgronomyAnimal scienceFood scienceEcologyBacteriaFermentation

Abstract

fetched live from OpenAlex

Methanogens are the only known microorganisms capable of methane production, making them of interest when investigating methane abatement strategies. A number of experiments have been conducted to study the methanogen population in the rumen of cattle and sheep, as well as the relationship that methanogens have with other microorganisms. The rumen methanogen species differ depending on diet and geographical location of the host, as does methanogenesis, which can be reduced by modifying dietary composition, or by supplementation of monensin, lipids, organic acids, or plant compounds within the diet. Other methane abatement strategies that have been investigated are defaunation and vaccines. These mitigation methods target the methanogen population of the rumen directly or indirectly, resulting in varying degrees of efficacy. This paper describes the methanogens identified in the rumens of cattle and sheep, as well as a number of methane mitigation strategies that have been effective in vivo.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.161

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 head0.045
GPT teacher head0.293
Teacher spread0.249 · 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