Nitrogen dynamics vary across hydrologic gradients and by forest community composition in the perhumid coastal temperate rainforest of southeast Alaska
Bibliographic record
Abstract
Nitrogen (N) limitation constrains plant growth, but complex interactions among species and ecosystems hinder our ability to identify primary drivers of N availability. Hydrologic, biogeochemical, and ecological processes interact spatially and temporally, requiring measurements of N across diverse ecosystem types and as a function of both site conditions and vegetation composition. We measured initial exchangeable and mineralized N along a hydrologic gradient in the Alaskan perhumid coastal temperate rainforest to test a conceptual model of linkages between N availability and landscape, hydrologic, and ecosystem characteristics in temperate forests. Mineralization was closely associated with inorganic N concentrations. Inorganic N as NH4+ generally increased with increasing depth to groundwater but was strongly determined by plant–water interactions. Exchangeable and mineralized N were closely linked to tree species, forest biomass, and hydrologic regime regardless of ecosystem type. The emergence of tree species as indicators of N cycling highlights the effect that species have on nutrient dynamics, while the trend of increasing inorganic N with increasing soil saturation points to the role of hydrology in driving N availability. Our research quantified N dynamics for an understudied, yet critical, system and provides a framework for exploring feedbacks among soil saturation, forest composition, and nutrient cycling in temperate forests.
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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 | 0.001 | 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".