Faculty Opinions recommendation of Ecosystem services altered by human changes in the nitrogen cycle: a new perspective for US decision making.
Bibliographic record
Abstract
Human alteration of the nitrogen (N) cycle has produced benefits for health and well-being, but excess N has altered many ecosystems and degraded air and water quality. US regulations mandate protection of the environment in terms that directly connect to ecosystem services. Here, we review the science quantifying effects of N on key ecosystem services, and compare the costs of N-related impacts or mitigation using the metric of cost per unit of N. Damage costs to the provision of clean air, reflected by impaired human respiratory health, are well characterized and fairly high (e.g. costs of ozone and particulate damages of $28 per kg NO(x)-N). Damage to services associated with productivity, biodiversity, recreation and clean water are less certain and although generally lower, these costs are quite variable (< $2.2-56 per kg N). In the current Chesapeake Bay restoration effort, for example, the collection of available damage costs clearly exceeds the projected abatement costs to reduce N loads to the Bay ($8-15 per kg N). Explicit consideration and accounting of effects on multiple ecosystem services provides decision-makers an integrated view of N sources, damages and abatement costs to address the significant challenges associated with reducing N pollution.Published 2011. This article is a US Government work and is in the public domain in the USA. PMID: 21624028
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.079 | 0.020 |
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".