Influence of moisture regime and tree species composition on nitrogen cycling dynamics in hardwood forests of Mammoth Cave National Park, Kentucky, USA
Bibliographic record
Abstract
Understanding how natural factors interact across the landscape to influence nitrogen (N) cycling is an important focus in temperate forests because of the great inherent variability in these forests. Site-specific attributes, including local topography, soils, and vegetation, can exert important controls on N processes and retention. Seasonal monitoring of N cycling dynamics was carried out for 2 years in deciduous forest stands that differed in soil moisture status and geologic substrate, and thus, in tree species composition to determine the effects of tree species composition, mediated by moisture and soil chemistry, on N cycling. Geologic substrate influenced soil and soil leachate chemistry but did not appear to affect N cycling in the upper 10 cm. Moisture status was strongly correlated with tree species composition, which was significantly related to N cycling parameters. Sugar maple was associated with high net nitrification rates and soil solution NO3 concentrations, whereas in oak stands nitrification was low and soil solution NO3 was at or near detection limits. Tree species composition in the understory suggests that sugar maple may be increasing in mesic sites and that oak regeneration in all sites is very limited, and a shift in species composition could result in changes to N retention and export.
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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) | 0.001 | 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".