Integrating silviculture with hemlock woolly adelgid mitigation: crown dynamics of <i>Tsuga canadensis</i> (L.) Carrière 10 years after thinning
Bibliographic record
Abstract
Pre-emptive silvicultural treatments can enhance host-tree vigor as a component of an integrated management approach to mitigate invasive species’ impacts. Hemlock woolly adelgid (HWA, Adelges tsugae Annand, 1928) threatens the health and existence of eastern hemlock-dominated forests. We propose that increasing canopy light exposure via silvicultural thinning can reduce hemlock’s vulnerability to HWA. Three stands with high hemlock densities on the Allegheny National Forest were selected for thinning. There was no HWA infestation and half of each stand was thinned. Circular plots were established surrounding 96 hemlock “subject trees” targeted for crown release on 3–4 sides. Similar criteria were used to select 90 hemlocks in untreated plots. We visited subject trees pre-thinning and 5 and 10 years post thinning and measured the following: diameter, total height, live crown ratio, and crown area. Temporal changes in diameter were not significant between treatments, but trees in thinned plots averaged 5 cm larger after 10 years. Live crown ratio was maintained in thinned stands and decreased after year 5 in controls. Crown area increased in thinned plots and decreased in controls. Residual basal area significantly influenced growth but varied between treatments and among 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) | 0.000 | 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".