Advances and Challenges for Nutrient Management in China in the 21st Century
Bibliographic record
Abstract
Managing agricultural nutrients to provide a safe and secure food supply while protecting the environment remains one of the great challenges for the 21st century. The fourth International Nutrient Management Symposium (INMS), held in 2011 at the University of Delaware, addressed these issues via presentations, panel sessions, and field tours focused on latest technologies and policies available to increase nutrient use efficiency. Participants from the United States, Europe, Canada, and China discussed global trends and challenges, balancing food security and the environment in countries with struggling and emerging economics, nutrient management and transport at the catchment scale, new technologies for managing fertilizer and manure nutrients, and adaptive nutrient management practices for farm to watershed scales. A particular area of interest at the fourth INMS was nutrient management progress and challenges in China over the past 40 years. China's food security challenges and rapidly growing economy have led to major advances in agricultural production systems but also created severe nutrient pollution problems. This special collection of papers from the fourth INMS gives an overview of the remarkable progress China has made in nutrient management and highlights major challenges and changes in agri-environmental policies and practices needed today. Lessons learned in China are of value to both developing and developed countries facing the common task of providing adequate food for an expanding world population, while protecting air and water quality and restoring damaged ecosystems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".