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Record W4388452207 · doi:10.25071/2817-5344/49

No More Cattle?

2023· article· en· W4388452207 on OpenAlexaff
M. Martel

Bibliographic record

VenueCanadian Journal for the Academic Mind · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsCarleton University
Fundersnot available
KeywordsAgricultureGreenhouse gasSustainabilityContext (archaeology)BusinessNatural resource economicsClimate changeAgricultural economicsLivestockConsumption (sociology)EconomicsGeographyEcology

Abstract

fetched live from OpenAlex

Anthropogenic climate change is undeniably altering planet Earth, and agricultural emissions are a significant contributor to this crisis. Agriculture, specifically cattle farming, is a key emitter of GHG emissions, accounting for 14.5% of global GHG emissions. This paper thus asks, given that cattle farming contributes a significant amount of total global GHG emissions, if the elimination of cattle farming in the EU is a sustainable way to reduce total GHG emissions. This paper explores the sustainability of cattle farming in the EU and highlights the vital role that EU cattle farming plays in the EU economy and in meeting the global food supply. It also explores the role that beef consumption plays in human diets. By researching the available literature, this paper finds that the complete elimination of cattle farming in the EU would have devastating effects on the EU economy and would leave the global food demand largely unmet. Not to mention that the environmental benefits of eliminating cattle farming become less significant when accounting for the emissions of new economic activity on ex-cattle grazing lands. Thus, this paper highlights the importance of improving cattle management and changing dietary patterns to mitigate GHG emissions in the context of a worsening climate and an increasing global food demand that must be met.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.019
GPT teacher head0.266
Teacher spread0.247 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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