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Record W4281835919 · doi:10.46380/rias.vol5.e215

Contribución de las emisiones de gas metano producidas por el ganado bovino al cambio climático

2022· article· es· W4281835919 on OpenAlexaff
Katherine Paola Tigmasa Paredes

Bibliographic record

VenueRevista Iberoamericana Ambiente & Sustentabilidad · 2022
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicAgricultural and Food Production Studies
Canadian institutionsImpact
Fundersnot available
KeywordsPolitical scienceHumanitiesEnvironmental sciencePhilosophy

Abstract

fetched live from OpenAlex

El sector ganadero es uno de los principales sistemas que contribuyen al desarrollo sostenible de la agricultura, sus principales aportaciones están en la seguridad alimentaria, la nutrición y el crecimiento económico, sin embargo, este sector es responsable de la emisión de una gran cantidad de gases de efecto invernadero. Este trabajo tuvo la finalidad de mostrar las contribuciones que presenta la producción ganadera frente al cambio climático, identificando la principal fuente de contaminación del sector, así como las alternativas de mitigación para la problemática presente. Para ello se analizaron los temas relacionados de diferentes fuentes, obteniendo información relevante para el sustento del trabajo. Como resultado, se pudo evidenciar que las emisiones de gas metano producidas por el ganado bovino es uno de los factores que contribuyen al cambio climático, por lo que se ha convertido en un problema a nivel global debido a sus impactos negativos.

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)0.001
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 score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.254
Teacher spread0.240 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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