Nitrogen Footprint in China: Food, Energy, and Nonfood Goods
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
The nitrogen (N) footprint is a novel approach to quantify losses to the environment of reactive N (Nr; all species of N except N2) derived from human activities. However, current N footprint models are difficult to apply to new countries due to the large data requirement, and sources of Nr included in calculating the N footprint are often incomplete. In this study, we comprehensively quantified the N footprint in China with an N mass balance approach. Results show that the per capita N footprint in China increased 68% between 1980 and 2008, from 19 to 32 kg N yr(-1). The Nr loss from the production and consumption of food was the largest component of the N footprint (70%) while energy and nonfood products made up the remainder in approximately equal portion in 2008. In contrast, in 1980, the food-related N footprint accounted for 86% of the overall N footprint, followed by nonfood products (8%) and energy (6%). The findings and methods of this study are generally comparable to that of the consumer-based analysis of the N-Calculator. This work provides policy makers quantitative information about the sources of China's N footprint and demonstrates the significant challenges in reducing Nr loss to the environment.
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 | 0.000 | 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 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 it