Aspen plant community response to organic matter removal and soil compaction
Bibliographic record
Abstract
Organic matter removal and reduced soil aeration porosity during logging are important factors influencing the sustained productivity of managed forest ecosystems. We studied the 4-year effect of these factors on diversity and composition of a trembling aspen (Populus tremuloides Michx.) plant community in northeastern British Columbia, Canada, in a completely randomized experiment with three levels of organic matter removal (tree stems; stems and slash; stems, slash, and forest floor) and three levels of soil compaction (none; intermediate (2-cm impression); heavy (5-cm impression)). Tree stem removal caused the greatest change in species diversity (30% of variance; ANOVA p ≤ 0.01), increasing the dominance of aspen and Calamagrostis canadensis (Michx.) Beauv. over other species. Slash removal had little effect. Forest floor removal caused the greatest compositional change (37% of variance; MANOVA p = 0.001), favoring ruderal over bud-banking species. Presence or absence of forest floor better explained these changes than any soil physical or chemical parameter. Although dominance of aspen over Calamagrostis was positively correlated with soil aeration porosity (R2 = 0.50, n = 27, p < 0.001), there were few differences between intermediate and heavy compaction. In this ecosystem, disturbances that reduce forest floor thickness without compacting soils will likely optimize plant species diversity and enhance aspen regrowth.
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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".