Xylogenesis under future climates: enhanced growth of balsam fir in a warming boreal forest
Bibliographic record
Abstract
Understanding the effects of climate variability on tree growth is crucial to predict forest carbon sequestration under global climate change. This study investigates the dynamics of wood formation in balsam fir in response to historical climatic data and projected variations. Weekly microcores were collected from 33 permanent plots in a boreal forest in Québec, Canada, over five growing seasons from 2018 to 2022. Warmer spring temperatures were associated with earlier cell enlargement initiation and increased cell production, whereas precipitation exerted a limited effect. An advancement in the onset of cell enlargement by 14-42 days and a 25-85% increase in cell production from 2051-2080 relative to the reference period (1981-2010) were predicted. Our results reveal potential shifts in growth dynamics and xylem production that could modify the growth processes in balsam fir, including carbon sequestration in the boreal forest ecosystems of Eastern Canada.
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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 | 0.000 | 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".