Leading‐edge disequilibrium in alder and spruce populations across the forest–tundra ecotone
Bibliographic record
Abstract
Abstract The distribution and composition of Arctic vegetation are expected to shift with ongoing climate change. Global models generally predict northward shifts in high‐latitude ecotones, and analysis of remote sensing data shows widespread greening and changes in vegetation structure across the circumpolar Arctic. However, there are still uncertainties related to the timing of these shifts and variation among different plant functional types. In this paper, we investigate disequilibrium dynamics of green alder and white spruce in the Tuktoyaktuk Coastal Plain, NWT. We used high‐resolution air photographs captured in the 1970s and 2000s to quantify changes in the distribution and abundance of alder and spruce near their northern limits. We found increases in alder and spruce stem density over time, but no change in their range limits, indicating that both species are affected by leading‐edge disequilibrium. Low stand density and temperature limitation of reproduction along the northern margin likely contributed to observed disequilibrium in both species. We also observed the greatest change in species occupancy within a burned area, suggesting that the increased frequency of fire will play a significant role in the timing and magnitude of near‐term vegetation change.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".