Recoupling Industrial Dairy Feedlots and Industrial Farmlands Mitigates the Environmental Impacts of Milk Production in China
Bibliographic record
Abstract
Dairy production is becoming more industrialized globally, especially in developing countries. The large amount of animal wastes from industrial feedlots cannot be fully used on nearby farmlands, leading to severe environmental problems. Using China as a case study, we found that most dairy feedlots employ a semicoupled mode that only recycles solid manure to farmlands, and only a few dairy feedlots employ a fully coupled mode that recycles both solid and liquid animal manure. To produce 1 ton of milk, the fully coupled mode could reduce greenhouse gas (including carbon dioxide, methane, and nitrous oxide in this paper) emissions by 24%, ammonia emissions by 14%, and N discharge into water by 29%, compared with the semicoupled systems. Coupling feedlots with constructed wetlands can further result in greater mitigation of N leaching into groundwater. However, the fully coupled system has not been widely used due to the low benefit to farmers and the institutional barrier that the feedlot owners have no right to use adjacent farmlands. Since a fully coupled system improves net ecosystem services that favor the public, a policy that supports removing the economic and institutional barriers is necessary. Our approach provides a template for mitigating environmental impacts from livestock production without sacrificing milk production.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Environmental study of recoupling dairy feedlots and farmlands to reduce the impacts of milk production in China.
It studies environmental impacts of dairy production systems, not research itself.
Environmental assessment of dairy manure coupling in China; agricultural production, not research.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".