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 (NO 3 -N, NH 4 -N, and PO 4 -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 NO 3 -N and NH 4 -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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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 teacher head, 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".