Assessing the Efficacy of Plant-Based Alternatives in Mitigating Climate Change
Bibliographic record
Abstract
Meat consumption and current livestock farming practices have a multitude of detrimental impacts on climate change and human health. Today, livestock farming is one of the largest contributors to greenhouse gas emissions (GHGs). The manure and chemicals used in livestock farms also seep into the water supplies and degrade the quality of water. Furthermore, livestock require a vast expanse of land for grazing and feeding, which leads to deforestation and habitat fragmentation. High meat consumption and its associated effects have also been implicated in causing various health complications in humans such as a higher prevalence of cardiovascular diseases, antimicrobial resistance (AMR), and an overall increase in mortality. Transitioning towards plant-based diets could not only mitigate the impacts of climate change, but it could also improve human health. This paper assesses the efficacy of transitioning towards plant-based diets and the overall benefits and challenges of this transition. This literature review is crucial as it compiles recent data about climate change and various studies about plant-based dietary transitions, as well as their impacts on the environment, human health, and climate change mitigation efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".