Increasing atmospheric nitrogen deposition: implications for tallgrass prairie restoration
Bibliographic record
Abstract
Continued intensification of agriculture and combustion of fossil fuels will increase rates of atmospheric nitrogen (N) deposition over the next century. N is typically a limiting resource for terrestrial plants, and many species are adapted to low-N conditions. Increased N availability can affect both plant biomass and species composition, often favouring N-demanding, adventive species. These effects can be adverse in the context of ecological restoration, where the end product often relies on establishing a particular community composition. I used a field experiment in Norfolk County, Ontario, to examine how N addition affects species composition and plant productivity of a tallgrass prairie restoration. I predicted that N addition would increase the abundance of plant species not included in the original seeding. Contrary to my prediction, relative abundance of native, rather than adventive species, increased with N addition, although the latter species were scarce at the site, possibly as a result of dispersal limitation. I conclude that increased N availability can enhance the growth of tallgrass prairie species in the first few years of restoration.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".