Impacts of Large-Scale Land-Use Change on the Uptake of Polycyclic Aromatic Hydrocarbons in the Artificial Three Northern Regions Shelter Forest Across Northern China
Bibliographic record
Abstract
This study quantifies the influence of large-scale land-use change induced by the artificial Three-Northern Regions Shelter Forest (TNRSF) across northern China on the environmental cycling of organic chemicals. Atmospheric removal and long-term trends of two polycyclic aromatic hydrocarbon (PAH) species, phenanthrene (PHE) and benzo[ a ]pyrene (BaP), resulting from increasing vegetation coverage and soil organic carbon in the TNRSF over the last two decades were examined. Field sampling data and modeling result showed that the total atmospheric removal of PHE by TNRSF increased from 36.4 tons in 1990 to 76.8 tons in 2010, increasing at a rate of 5.6% yr –1, and BaP from 2.2 to 4.5 tons, increasing at a rate of 5.2% yr –1 . Three model scenarios were designed to distinguish the effects of atmospheric emissions, and with and without TNRSF on the environmental fate of PAHs. Approximately 1–4% of PHE and BaP emitted in northern China were removed by the TNRSF during 1990–2010. Model simulations revealed that the TNRSF enhanced atmospheric removal of PHE by 29% and BaP by 53% compared with the simulation without the TNRSF, manifesting marked contributions of land-use change by the artificial TNRSF, the largest afforestation activity in human history, to the atmospheric removal of organic chemicals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".