DECADAL REEVALUATION OF SUGAR MAPLE DIEBACK ETIOLOGY ACROSS THE UPPER GREAT LAKES REGION
Bibliographic record
Abstract
Sugar maple (Acer saccharum) is a foundational tree species in northern hardwood forests of the United States and Canada. Though previous work has documented areas of substantial stress for this species in eastern North America, increasing reports of crown dieback in the Upper Great Lake states through the early 2000’s highlighted the relative lack of understanding of regional trends and causes. A 120-plot network of maple forest health monitoring sites was established and annually visited across Upper Michigan, northern Wisconsin, and eastern Minnesota between 2009 and 2012 to catalog and understand the regional phenomenon. Results from the project’s initial years (Phase I) determined a significant correlation between sugar maple dieback and interrelated forest floor conditions (earthworm impact rating, soil carbon, herbaceous cover, and soil manganese) known to be influenced by exotic earthworms. Ten years later, the network was resurveyed in 2021 and 2022 (Phase II). Sampling methods replicated prior methodology and added additional damaging agent signs and symptoms, including ungulate browse, lecanium species (Parthenolecanium spp.) and cottony maple scale (Pulvinaria innumerabilis), as well as more detailed sampling of earthworm species abundance, diversity, and impact. Resurvey data suggest earthworm impact rating is still significantly correlated with sugar maple dieback across the network. Sugar maple dieback is ongoing and is 15.4% per tree averaged at the plot level (compared with 12.4% ten years ago) across the study area, though it is highly variable. Also, average canopy dieback for residual trees in harvest treatments worsened over the intervening years. Scale are not apparently linked to dieback condition. Future uses for the data include amendment of risk maps that land managers can incorporate into treatment plans using key correlates of decreased sugar maple health and vigor.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".