Dendroclimatic analysis of central Appalachian red spruce in West Virginia
Bibliographic record
Abstract
We conducted dendroclimatic analyses and constructed future growth projections for red spruce (Picea rubens Sarg.) throughout the central Appalachians in the state of West Virginia. This study involved field sampling of 18 sites across red spruce’s range in six regions throughout the Monongahela National Forest that were selected based on pairwise combinations of three latitudinal groups (north, central, and south) with two aspects (north and south). Each combination of latitudinal group and aspect was referred to as a landscape cluster. Growth was negatively impacted by high summer temperature stress but responded favorably to high fall temperatures. The results also suggested that red spruce was likely impacted by the degree of winter harshness in all landscape clusters. In the northern latitudinal landscape clusters, red spruce responded favorably to warm spring temperatures by allowing an early start to the growing season. Growth projections under a future climate change scenario show that future expected increases in mean and maximum monthly temperatures will have negative effects on future spruce growth. The forecasting results suggested that red spruce in northern latitudes on south aspects or central latitudes on north aspects are the landscape clusters that will likely be the most resilient to future climate change. Dendroclimatic results and future growth projections can assist with identifying locations that are most suitable for future red spruce restoration activities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".