Sustainable intensification of livestock systems using forage legumes in the Anthropocene
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.
Bibliographic record
Abstract
Abstract Sustainable intensification of livestock systems implies greater efficiency in resource utilization resulting in greater output of products and other ecosystem services per unit of resource input. Integrating forage legumes into livestock systems is a viable way to reduce the input of industrial N fertilizer, reducing the use of fossil fuels and helping to mitigate global warming, a major problem during the Anthropocene. Some forage legumes have greater concentrations of secondary compounds, such as condensed tannins, that might reduce the emission of greenhouse gases (GHG) from ruminant eructation and excreta. Furthermore, forage legumes might enhance cattle performance because of greater nutritive value, resulting in greater production per unit of GHG released. Shortening the production cycle and improving cattle reproductive efficiency could have a major impact on reducing the overall carbon footprint of the system. Grazing systems with more diversified plant species are typically more resistant and resilient, adapting to current climate changes during the Anthropocene. Novel technologies might accelerate the development of future grazing systems using forage legumes as a key component. Breeding efforts for the next‐generation legumes must focus on adaptation and potential use for mitigation of negative environmental impacts. There are examples of successful integration of forage legumes into livestock systems in different regions of the world, with a major reduction in off‐farm inputs and maintaining the system productive. These successful examples could be used to increase adoption and improve the efficiency of current livestock systems.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it