Labrador Sea's influence on black spruce forests: insights from tree-ring stable oxygen isotopes
Bibliographic record
Abstract
Abstract Black spruce (Picea mariana Mill. B.S.P.), is a dominant species emblematic of eastern Canada's boreal forests. It grows in a vast array of climatic conditions ranging from cold maritime-oceanic climates near the Labrador Sea shore to cold continental conditions in the central portions of Quebec-Labrador. However, along the continentality gradient, timing and provenance of heat and moisture that support growth are uncertain, weakening our capacity to describe and predict the response of boreal ecosystems to climate variability. Here, we measured oxygen isotope composition in tree-ring cellulose of black spruces from three sites, and provide evidence of a direct influence of the Labrador Sea on adjacent continental ecosystems. Our results report a landwards decrease in δ18Otrc, a pattern that is also visible in the simulated oxygen isotope composition of precipitation water (δ18Op). We also reveal a landwards decoupling between δ18Otrc variability (1950-2013) and maximum temperature (Tmax) variations over the Northwest Atlantic. Our results imply that tree-ring oxygen isotopes may be of great help to circumscribe the spatial extent of Labrador Sea's influence on surrounding boreal ecosystems. Moreover, our study reveals that despite of their apparent ecological homogeneity, eastern Canada’s black spruce forests clearly rely on heterogeneous sources of heat and moisture.
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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".