Predicting forest dieback in Maine, USA: a simple model based on soil frost and drought
Bibliographic record
Abstract
Tree roots of northern hardwoods are shallow rooted, winter active, and minimally frost hardened; dieback is a winter freezing injury to roots incited by frost penetration in the absence of adequate snow cover and exacerbated by drought in summer. High soil water content greatly increases conductivity of frost. We develop a model based on the sum of z-scores of soil frost (December–February) and drought in summer (May–September) that accurately predicts timing and severity of dieback on sugar maple ( Acer saccharum Marsh.), Betula spp., Fraxinus spp., and red spruce ( Picea rubens Sarg.) in Maine, USA, during 1920–1995. Discrepancies between predicted and actual dieback relate largely to tree population dynamics. Extraordinary levels of dieback on Betula during 1935–1950 occurred under low stress but in mature populations; under high climate stress during 1951–1975, younger surviving Betula proved resistant and showed little dieback. Severe dieback in Acer stands during 1975–1995 may relate to extensive partial cutting, leaving stands open to frost and high water tables. Widespread use of forwarder feller/buncher and wheeled skidders after 1960 commonly left hummocky, irregular surfaces subject to pooling and accelerated frost penetration. Silvicultural applications include modifying harvesting practices to minimize frost complications and use of the model to track and map climatic risk to avoid conditions and locales subject to dieback.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".