Distribution of white spruce lateral fine roots as affected by the presence of trembling aspen: root mapping using simple sequence repeat DNA profiling
Bibliographic record
Abstract
Mixedwood forests of white spruce ( Picea glauca (Moench) Voss) and trembling aspen ( Populus tremuloides Michx.) may possess ecological advantages over monospecific white spruce stands. Belowground competition may be reduced through vertically stratified roots; facilitation of growth may occur in upper soil layers through nutrient-rich trembling aspen litterfall. These effects may incentivize white spruce to preferentially exploit upper soil layers in mixedwood stands, resulting in wider root systems. This research contrasted white spruce fine root (diameter <2 mm) distributions in organic layers of white spruce and mixedwood stands. Research occurred at the Fallingsnow Ecosystem Project site in northwestern Ontario. Eighteen plots represented mixedwood and pure white spruce stands. Trees were mapped; foliage and root samples were collected. Roots were separated by species and scanned to determine length. Simple sequence repeat DNA profiles were determined for all white spruce trees and for 45 white spruce root fragments per plot. Root and tree DNA profiles were matched; corresponding distances were calculated. Most (80%) root fragments were within 3.2 m of tree stems. Root prevalence decreased rapidly with distance. Organic layer pH was significantly less acidic in mixedwood plots, but only in one block. A subtle significant trend towards wider root distributions occurred in mixedwood stands.
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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 | 0.000 |
| 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 | 0.000 |
| 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".