Identifying roots of northern hardwood species: patterns with diameter and depth
Bibliographic record
Abstract
Forest canopies are often stratified by species; little is known about the depth distribution of tree roots in mixed stands because they are not readily identified by species. We used diagnostic characteristics of wood anatomy and gross morphology to distinguish roots by species and applied these methods to test for differences in the rooting depth of sugar maple ( Acer saccharum Marsh.), American beech ( Fagus grandifolia Ehrh.), and yellow birch ( Betula alleghaniensis Britt.) in two northern hardwood forests. We also distinguished hobblebush ( Viburnum lantanoides Michx.) and white ash ( Fraxinus americana L.) roots. Analysis of plastid DNA fragment lengths confirmed that 90% of the roots were correctly identified. The vertical distribution of fine roots of these species differed by 2–4 cm in the median root depth (P = 0.03). There was a significant difference in the distribution of roots by size class, with fine roots (0–2 mm) being more concentrated near the soil surface than coarser roots (2–5 mm; P = 0.004). The two sites differed by <2 cm in median rooting depths (P = 0.02). The visual identification of roots for the main tree species in the northern hardwood forest allows species-specific questions to be posed for belowground processes.
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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) | 0.001 | 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".