Nutrient concentrations of roots vary with diameter, depth, and site in New Hampshire northern hardwoods
Bibliographic record
Abstract
Roots are important to ecosystem nutrient pools and fluxes, but they are difficult to sample for tissue analysis, especially at depth. We analyzed patterns of nutrient concentrations in live roots up to 20 mm in diameter collected from quantitative soil pits in six northern hardwood sites at the Bartlett Experimental Forest, New Hampshire, USA. Root concentrations of nitrogen (N), phosphorus (P), calcium (Ca), and magnesium (Mg) were higher in the forest floor than in the mineral soil, by 23%–61% in fine roots (0–1 mm and 1–2 mm in diameter). Using only samples collected from the O horizon to characterize roots throughout the profile resulted in an average error across all elements of 16% in estimates of root nutrient contents. Within the mineral soil, there was little difference in root nutrient concentrations with depth. There were significant patterns with root diameter: N and Mg concentrations were highest in the finest roots, while Ca concentrations peaked in the 2–5 mm diameter class. One site (C8) differed from the others in having lower N but higher P, Ca, Mg, and potassium (K) concentrations in roots. In summary, analyzing roots by site and diameter class is more important to accurate nutrient accounting than is analyzing roots from depth in the mineral soil, but roots in the forest floor and the mineral soil differ dramatically for some elements.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".