Aspen and white spruce productivity is reduced by organic matter removal and soil compaction
Bibliographic record
Abstract
Declines in forest productivity have been linked to losses of organic matter and soil porosity. To assess how removal of organic matter and soil compaction affect short-term ecosystem dynamics, pre-treatment and year 1, 5 and 10 post-treatment soil properties and post-treatment plant community responses were examined in a boreal trembling aspen (Populus tremuloidesMichx.)-dominated ecosystem in northeastern British Columbia. The experiment used a completely randomized design with three levels of organic matter removal (tree stems only; stems and slash; stems, slash and forest floor) and three levels of soil compaction (none, intermediate [2-cm impression], heavy [5-cm impression]). Removal of the forest floor initially stimulated aspen regeneration and significantly reduced height growth of aspen (198 cm compared to 472–480 cm) as well as white spruce (Picea glauca [Moench] Voss) height (82 cm compared to 154–156 cm). The compaction treatments had no effect on aspen regeneration density. At Year 10, heights of both aspen and white spruce were negatively correlated with upper mineral soil bulk density and were lowest on forest floor + whole tree removal treatments. Recovery of soil properties was occurring in the 0 cm to 2 cm layer of mineral soil. Bulk density values for the 0 cm to 10 cm depth remained above 86% of the maximum bulk density for the site, a soil condition where reduced tree growth can be expected.
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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 | 0.001 | 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".