Selective herbivory on a nitrogen fixing legume (<i>Lathyrus venosus</i>) influences productivity and ecosystem nitrogen pools in an oak savanna
Bibliographic record
Abstract
Herbivory is known to change the structure of vegetation, but the possible effects of herbivory on ecosystem nitrogen pools are not well documented. Here we report that 13 years of deer exclusion significantly influenced ecosystem nitrogen pools and caused ecosystem productivity to more than double in a regularly burned Minnesota oak savanna. Herbivore exclusion greatly increased the abundance of Lathyrus venosus, a native nitrogen fixing legume. Primary productivity also increased through time, as did total soil nitrogen. This increase in productivity did not occur in unfenced plots, where there was a loss of total soil nitrogen, probably because fire-induced nitrogen losses exceeded gains. This study documents that herbivores, through “top-down” effects on foodwebs, can strongly influence nitrogen pools in terrestrial ecosystems, and that legumes can play a critical role in replacing fire-induced nitrogen losses in Midwestern oak savannas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".