Belowground competition influences growth of natural regeneration in thinned Douglas-fir stands
Bibliographic record
Abstract
Using a factorial combination of understory removal and trenching treatments, we examined the influences of belowground competition from understory and overstory vegetation on growth of naturally established Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco var. menziesii) saplings in thinned stands of Douglas-fir on moderately productive, glacially derived soils near Tacoma, Washington. Under limited light (26% ± 16% of full sun), sapling height and diameter growth were significantly reduced by belowground competition from overstory trees. Regardless of presence or absence of belowground competition from trees, understory vegetation did not have a detectable effect on sapling growth. Nitrogen deficiency in saplings was not detected in the presence of belowground competition: where tree roots were excluded, foliar nitrogen concentration and content increased without an increase in foliar mass. Belowground competition from overstory trees had a greater negative effect on growing season soil water content than did understory vegetation. Under the conditions of restricted light availability in this study, limitations in soil water content from competition had a strong growth-limiting effect on Douglas-fir regeneration. As potential canopy trees in a future uneven-aged stand, this sapling cohort would benefit from root gaps created during harvests.
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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".