Recent changes in treeline forest distribution and structure in interior Alaska
Bibliographic record
Abstract
Although the forest-tundra boundary is likely to be sensitive to future climate warming, the degree to which treeline response may lag climate change and the extent to which sensitivity to climate may vary among sites remain largely unknown. We used tree-ring analysis to reconstruct white spruce (Picea glauca) density from 1800 to present at and beyond the current forest limit at seven altitudinal treeline sites in two regions of interior Alaska. Treeline advance was ubiquitous: cone-bearing spruce are present beyond the current forest limit at all but one site, and tree density has increased at and beyond the forest limit in recent decades at all sites. Increases in stand density were positively correlated with summer temperature at most, but not all, sites. The timing of inferred advances in treeline differed significantly between regions, beginning in the mid- to late 1800s in the White Mountains and in the mid-1900s in the Alaska Range. These differences in the timing of treeline advance may be caused by differences in the rate of forest response to climate or by differences in regional climate history, which remains poorly known. Despite the variation in timing of an advance of treeline, the similarities among sites in the pattern (if not the timing) of change at treeline suggest that recent shifts in the location of the forest-tundra border are a widespread response to recent warming in Alaska.
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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) | 0.000 | 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".