Correlations between soil nutrient availability and fine-root biomass at two spatial scales in forested wetlands with contrasting hydrological regimes
Bibliographic record
Abstract
We investigated the relationship between soil nutrients and fine-root biomass at broad (among ecosystem types) and fine (within a 20 m × 20 m plot) spatial scales in forested wetlands of the southeastern United States. We selected three replicates each of high-fertility floodplain swamps, low-fertility depressional swamps, and intermediate-fertility river swamp sloughs and measured soil nutrient availability (NO3-N, NH4-N, and PO4-P) and fine-root biomass. At one replicate of each wetland type, a dense network of sampling points was used to measure variability (variance and coefficient of variation) of soil nutrients and fine-root biomass. At the broad scale, fine-root biomass was lower in floodplain swamps than in either river swamp sloughs or depressional swamps. Also, multiple linear regression and Spearman's rank correlations indicated a negative relationship between soil nutrient availability and fine-root biomass. Fine-scale correlates between soil nutrient availability and fine-root biomass were generally weak. Fine-scale variability of NO3-N and NH4-N was greatest in the floodplain swamps, but nutrients were not spatially patchy at any of the sampled sites. We conclude that soil nutrient availability may control fine-root biomass at the broad scale, but it is unclear if the same is true at fine spatial scales.
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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 | 0.001 |
| 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".