Dendroclimatic response of a coastal alpine treeline ecotone: a multispecies perspective from LabradorThis article is a contribution to the series Tree recruitment, growth, and distribution at the circumpolar forest–tundra transition.
Bibliographic record
Abstract
Coastal alpine forests are highly vulnerable to oceanic climate trends, yet these diverse environmental interactions remain poorly understood. We used a multispecies perspective to try to better assess the radial growth response of alpine treeline species within the Northeast Atlantic region of North America to climate variables using bootstrapped correlation analysis. The four species present, black spruce (Picea mariana (Mill.) B.S.P.), white spruce (Picea glauca (Moench) Voss), balsam fir (Abies balsamea (L.) Mill.), and eastern larch (Larix laricina (Du Roi) K. Koch) were sampled in an effort to capture tree–climate sensitivity that is representative of this entire alpine treeline. The climate–growth relationships of spruce trees were comparable with those reported in other Labrador studies, but spring drought sensitivity as reported for coastal northern white spruce trees was not observed. Rather, high levels of precipitation suggest that drought did not limit the radial growth of any of the four species. The relatively small number of statistically significant correlations between monthly climate variables and fir and larch trees suggests that factors other than climate limit their radial growth. The multispecies approach better highlighted the range of species-specific relationships between alpine treeline forests and maritime climates (monthly temperature and precipitation) found at the treeline ecotone.
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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 | 0.001 | 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) | 0.001 | 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".