Predicting xylem phenology in black spruce under climate warming
Bibliographic record
Abstract
In the next century, the boreal ecosystems are projected to experience greater rates of warming than most other regions of the world. As the boreal forest constitutes a reservoir of trees of huge ecological importance and only partially known economic potential, any possible climate-related change in plant growth and dynamics has to be promptly predicted and evaluated. A model for assessing xylem phenology in black spruce [Picea mariana (Mill.) B.S.P.] using daily temperatures and thermal thresholds was defined and applied to predict changes in onset, ending and duration of xylem growth under different warming scenarios with temperatures rising by up to 3 °C. This was achieved by collecting and analyzing a dataset obtained from a 7-year monitoring of cambium phenology and wood formation on a weekly time-scale in trees growing in four sites at different latitudes and altitudes in the Saguenay-Lac-Saint-Jean region (Quebec, Canada). The onset of xylem growth occurred between mid-May and early June while the end ranged between mid-September and early October, resulting in a growing season of 101–141 days. The model predicted longer duration of xylem growth at higher temperatures, with an increase of 8–11 days/ °C, because of an earlier onset and later ending of growth. With an increase of 3 °C in the mean temperature during the year, the duration of xylem growth changed on average from 125 to 160 days. The predicted changes in cambial phenology could significantly affect future wood production of the boreal ecosystems.
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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) | 0.001 | 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) | 0.000 | 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".