Wood anatomical traits reveal water constraints on black spruce xylem formation in the boreal ecosystem
Bibliographic record
Abstract
One of the most evident effects of the rapid warming occurring recently in cold and high-latitude forests is the lengthening of the growing season, with the concurrent extension of the period of wood formation. In addition, the increase of evapotranspiration demand is starting to induce drought-stress conditions. By selecting the same black spruce (Picea mariana) trees (20 trees in 4 sites along a latitudinal gradient in Quebec, Canada) where xylogenesis analyses were performed in the last years, we investigated long-term series of wood anatomical traits and compare them to previous short-term findings. \nTime series of wood anatomical traits were correlated to chronologies of daily temperature, VPD and precipitation during the period 1936-2010. In all sites, tracheid area correlated negatively with June-September temperature and VPD, and positively with precipitation. Meanwhile, cell-wall thickness and the number of cells per ring in the northernmost site were positively affected by spring and summer temperature. \nWhile previous monitoring studies evidenced temperature as the key climate variable influencing the timing of xylogenesis phases, our results show that water availability plays a central role in shaping xylem cell features in boreal black spruce. This stresses the importance of an integrate approach to better understand the relationships between wood formation and climate variability at both intra-annual and long (decadal) time scales. This approach will hopefully reduce the uncertainties and skewed interpretations of models on how boreal forest will perform in the future.
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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 | 0.000 |
| 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".