Multi-scale Influence of Snowmelt on Xylogenesis of Black Spruce
Bibliographic record
Abstract
Snowmelt is considered to affect growth of the boreal forest. So, we tested the hypothesis that late snowmelts delay the onset of xylogenesis and reduce xylem production in trees. Timings of xylem formation were compared to the dates of complete snowmelt combining a 7-year monitoring of cambial activity with meteorological records in four plots of Picea mariana in Quebec, Canada. The spatial and temporal variability in snowfall was analyzed separately, so taking into account both the long- and short-term effects. Snowfall occurred from October to May, with a snow cover lasting 173–199 days. Overall, xylogenesis lasted 99–117 days, with onsets ranging from late May to mid-June. The highest cell productions were observed in the warmest site, where the longest periods of growth were observed. Although at long-term the effects of snowmelt were significant for both onset and duration of xylogenesis and cell production, at short-term only the relationship between the onset of xylogenesis and the date of complete snowmelt was significant. The initial hypothesis could be confirmed only partially. The different responses to the long- and short-term analyses demonstrate the multi-scale influence of snowfall on tree growth and the determinant role of nutrient cycling in the productivity of 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) | 0.000 | 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".