Predicting the impact of hemlock woolly adelgid on carbon dynamics of eastern United States forests
Bibliographic record
Abstract
The hemlock woolly adelgid (HWA; Adelges tsugae Annand) is an introduced insect pest that threatens to decimate eastern hemlock ( Tsuga canadensis (L.) Carriere) populations. In this study, we used the ecosystem demography model in conjunction with a stochastic model of HWA spread to predict the impact of HWA infestation on the current and future forest composition, structure, and carbon (C) dynamics in the eastern United States. The spread model predicted that on average the hemlock stands south and east of the Great Lakes would be infested by 2015, southern Michigan would be reached by 2020, and northeastern Minnesota by 2030. For the period 2000–2040, the ecosystem demography model predicted a mean reduction of 0.011 Pg C·year–1 (Pg C = 1015 g C), an 8% decrease, in the uptake of carbon from eastern United States forests as a result of HWA-caused mortality, followed by an increased uptake of 0.015 Pg C·year–1 (a 12% increase) in the period 2040–2100, as the area recovers from the loss of hemlock. Overall, we conclude that while locally severe, HWA infestation is unlikely to have a significant impact on the regional patterns of carbon fluxes, given that eastern hemlock represents a limited fraction of the standing biomass of eastern forests and that it has relatively low productivity compared with the tree species that are likely to replace it.
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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 | 0.001 | 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".