Thinning effects on foliar elements in eastern hemlock: implications for managing the spread of the hemlock woolly adelgid
Bibliographic record
Abstract
Stand thinning is being tested as a means to limit the impacts of the invasive hemlock woolly adelgid (HWA; Adelges tsugae Annand) on eastern hemlock (Tsuga canadensis (L.) Carriere). The efficacy of this strategy may be reduced if thinning increases hemlock foliar nutrients because HWA densities are correlated with foliar concentrations of N, P, K, Ca, and Mn. We determined foliar N, P, K, Ca, and Mn concentrations in 1-year-old and all other (older) needles prior to and for 4 years after thinning in northwestern Pennsylvania stands of eastern hemlock without HWA. Average foliar concentrations in 1-year-old needles were 1.30–1.80 g N·100 g−1, 1300–1700 mg P·kg−1, 4200–6300 mg K·kg−1, 2500–5200 mg Ca·kg−1, and 2393 μg Mn·g−1. N, P, and K decreased, Ca increased, and Mn first increased and then stabilized. Thinning by itself did not affect the tested foliar nutrients. The interaction between treatment and year was significant and evident in temporal trajectories of foliar N and K. However, the differences between thinned and unthinned plots within years averaged only 0.03 g N·100 g−1 and 340 mg K·kg−1. We concluded that even though thinning changed the temporal trajectories of foliar N and K, the nutritional shifts were minimal, brief, and unlikely to affect the efficacy of thinning in limiting the impacts of HWA.
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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 | 0.001 | 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".