The vertical and horizontal distribution of roots in northern hardwood stands of varying age
Bibliographic record
Abstract
Coring methods cannot reveal the distribution of roots with depth in rocky soil, and fine roots are typically sampled without regard to the location of trees. We used quantitative soil pits to describe rooting patterns with soil depth and distance to trees in northern hardwood stands. We sited three 0.5 m2 quantitative soil pits in each of three young (1927 years) and three older (5669 years) stands developed after clear-cutting. Live roots were divided into diameter classes delimited at 0.5, 1, 2, 5, 10, 20, and 100 mm; dead roots were not distinguished by size. Mean total live-root biomass was 2900 ± 500 g·m2 in older stands and 1500 ± 400 g·m2 in young stands. The root mass in the 220 mm class was 2.7 times greater in the older stands (p = 0.03); fine-root (<2 mm) biomass was 1.5 times greater (p = 0.12), suggesting that fine-root biomass continues to increase past the age of canopy closure in this forest type. Root biomass density declined with soil depth, with the finest roots (<0.5 mm) declining most steeply; roots were found at low densities well into the C horizon. We analyzed root biomass density as a function of the influence of nearby trees (represented as the sum of basal area divided by the distance from the pit) and found that fine as well as coarse roots reflected this influence. In systems where this is the case, root measurements should be made with attention to patterns of tree distribution.
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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".