Nitrogen availability and forest productivity along a climosequence on Whiteface Mountain, New York
Bibliographic record
Abstract
We studied broadleaf and needle-leaf forests along an elevation gradient (6001200 m) at Whiteface Mountain, New York, to determine relationships among temperature, mineral N availability, and aboveground net primary productivity (ANPP) and controls on the latter two variables. We measured net N mineralization during the growing season, annual litterfall quantity and quality, aboveground woody biomass accumulation, and soil organic matter quality. Inorganic N deposition from cloudwater markedly increases mineral N availability above 1000 m in this region. Consequently, mineral N availability across the climosequence remains relatively constant because N mineralization decreases with increasing elevation. Across this climosequence, air temperature (as growing season degree-days) exerted the most control on ANPP. Nitrogen mineralization was most strongly related to soil growing season degree-days and less so to lignin to N ratios in litter. ANPP was correlated with N mineralization but not with mineral N availability. Combining our data with those from similar studies in other boreal and cool temperate forests shows that N mineralization and ANPP are correlated at local, regional, and interbiome scales. Regarding the persistent question concerning cause and effect in the N mineralization forest productivity relationship, our data provide evidence that at least in this case, forest productivity is a control on N mineralization.
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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 | 0.001 | 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".