The spatial influence of <i>Pseudotsuga menziesii</i> retention trees on ectomycorrhiza diversity
Bibliographic record
Abstract
Living retention trees are being used in managed forests to promote a variety of values, including the maintenance of biological diversity. Federal forest plans for the northwestern USA include guidelines that require the retention of a minimum of 15% basal area in harvest units, with the goal of facilitating the development of late-seral stand structure, which is an important habitat element for old-growth forest-dependent species. However, effective levels and patterns of green-tree retention are unknown. We present results of a treatment consisting of 15% basal area, evenly dispersed retention (15%D). We quantified changes in the ectomycorrhiza (EM) community after the 15%D treatment, both near and away from retention trees. Pretreatment samples were obtained between 1 and 24 months before tree harvest. Post-treatment samples were collected within 14–25 months of harvest. In areas 8–25 m from retention trees, there was a 50% decline in the number of EM types per soil core from before to after treatment. Soil cores taken >5 m from retention trees exhibited a shift in EM community structure. EM-type richness was positively correlated with fine-root-tip density. We demonstrate the potential for retention trees to act as refugia for recolonization of newly established seedlings by ectomycorrhizal fungi.
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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 | 0.000 | 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".