Cross-site comparison of ecosystem nutrient cycling in hardwood and conifer forests on contrasting soils
Bibliographic record
Abstract
In the northeastern United States, both hardwood and conifer forests have developed on sites with contrasting soils, allowing an examination of the effect of site and forest type on ecosystem nutrient cycling. We measured biomass production and nutrient fluxes in northern hardwood and conifer stands at three sites differing in soil fertility. We found that leaf, root, and wood concentrations of calcium (Ca), magnesium (Mg), and potassium reflected differences in soil base cation availability, while concentrations of nitrogen (N) and phosphorus (P) were more consistent across sites. Nutrient uptake was calculated as the sum of litterfall, net throughfall (throughfall minus precipitation), root turnover, and accumulation in perennial tissues (wood). We propose a novel metric of nutrient cycling, the nutrient retention fraction (NRF), defined as the proportion of annual nutrient uptake retained in biomass accretion. Because the NRF is unitless, it can be compared across nutrients; Ca and Mg had the highest NRF and P the lowest ( p = 0.05). Across sites and elements, NRFs were lower for conifers (5.0 ± 0.6%) than for hardwoods (7.2 ± 0.5%), associated with their lower productivity. Nutrient-use efficiency (biomass production divided by nutrient uptake) tended to be high where foliar concentrations indicated low availability of that nutrient. Nutrient retention of N and P was higher where availability of the other element was high, which could be a mechanism contributing to N and P co-limitation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".