Influence of elevation and site productivity on conifer distributions across Alaskan temperate rainforests
Bibliographic record
Abstract
We investigated the influence of landscape factors on the distribution and life stage stability of coastal tree species near the northern limit of their ranges. Using data from 1465 forest inventory plots, we estimated probability of occurrence and basal area of six common conifer species across three broad latitudinal regions of coastal Alaska. By also comparing models across life stages of each species (seedlings, saplings, mature trees, and dead trees), we explored trends in population stability at this leading edge of climate change. Elevation had a stronger influence on the probability of tree species occurrence than on basal area; site productivity impacted both estimated odds of presence and estimated basal area for most species in at least some regions. Interestingly, there were fairly dramatic differences across species in the degree to which the modeled probability of occurrence differed across the four life stages. Western redcedar (Thuja plicata Donn ex D. Don), for example, showed relatively stable distributions but other species appear to be in flux, e.g., yellow-cedar (Callitropsis nootkatensis (D. Don) D.P. Little), which has experienced widespread mortality at low elevations. Differential effects of elevation on live versus dead basal area suggest that mountain hemlock (Tsuga mertensiana (Bong.) Carrière) and yellow-cedar are shifting upslope in some regions and that Sitka spruce (Picea sitchensis (Bong.) Carrière) is shifting downslope in the Northwest region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".