The influence of basswood (<i>Tilia americana</i>) and soil chemistry on soil nitrate concentrations in a northern hardwood forest
Bibliographic record
Abstract
In the Arbutus Lake Watershed in the Adirondack Mountains, New York, two nearly adjacent catchments (14 and 15) varied significantly in volume-weighted stream water nitrate (NO3) export (54 and 17 μequiv.·L–1, respectively; P < 0.001). The most notable differences between the catchments were that Catchment 14 had significantly higher soil Ca concentrations and patches of basswood ( Tilia americana L.). We evaluated the possible contributions of basswood and soil Ca concentrations to soil water NO3 concentrations. Among the major overstory tree species, basswood leaf litter had the lowest C:N ratios, highest Ca concentrations, and among the lowest lignin:N ratios. Basswood basal area was significantly related to soil water NO3 concentrations (R = 0.46, P = 0.01). Forest floor and mineral soil Ca concentrations were positively correlated with basswood basal area and negatively correlated with American beech ( Fagus grandifolia Ehrh.) basal area. Our results suggest that a relatively low-density, calciphilic species such as basswood may create, given the proper soil conditions, hotspots with elevated soil water NO3 concentrations. These hotspots result from the convergence of high soil Ca concentrations, due mostly to soil geology, with relatively labile litter substrate available for N mineralization and nitrification.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".