Modeling the impacts of hemlock woolly adelgid infestation and presalvage harvesting on carbon stocks in northern hemlock forests
Bibliographic record
Abstract
To better understand the potential impact of the invasive hemlock woolly adelgid (HWA, Adelges tsugae Annand) and presalvage activities on carbon (C) dynamics in northern stands of eastern hemlock (Tsuga canadensis (L.) Carr.), we used the Forest Vegetation Simulator and Forest Inventory and Analysis data to model C storage and successional pathways under four scenarios: presalvage harvesting; HWA-induced mortality; presalvage harvesting plus HWA-induced mortality; and no disturbance (control). Our simulation showed that all treatments differed in total C storage in the short term, with HWA-induced mortality providing the highest total C storage due to regeneration and ingrowth of replacement species combined with retention of standing and downed deadwood. At the end of the 150-year simulation, all disturbance scenarios had significantly lower total C than the control. The cumulative net C gain was lower for the two presalvage scenarios than for the HWA scenario, indicating that allowing HWA to progress naturally through a stand may result in the least impact to long-term C sequestration and net C storage. While differences were not significant on low hemlock density stands, impacts to the estimated 267 000 ha of northeastern forests where hemlock is dominant could result in conversion to red maple (Acer rubrum L.) and a net loss of over 4 million metric tons of potentially sequestered C over the next 150 years.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".