Xylogenesis in black spruce: does soil temperature matter?
Bibliographic record
Abstract
In boreal ecosystems, an increase in soil temperature can stimulate plant growth. However, cambium phenology in trees was better explained by air than soil temperature, which suggested that soil temperature is not the main limiting factor affecting xylogenesis. Since soil temperature and snowmelt are correlated to air temperature, the question whether soil temperature directly limits xylogenesis in the stem will remain unresolved without experiments disentangling air and soil temperatures. This study investigated the effects of an increase of 4 °C in soil temperature and a consequent 1-week earlier snowmelt on growth of black spruce [Picea mariana (Mill.) BSP] in the boreal forest of Quebec, Canada. The soil of two natural stands at different altitudes was warmed up with heating cables during 2008-2010 and cambial phenology and xylem production were monitored weekly from April to October. The results showed no significant effect of the treatment on the phenological phases of cell enlargement and wall thickening and lignification. The number of cells produced in the xylem also did not differ between control and heated trees. These findings allowed the hypothesis of a direct influence of soil temperature on stem growth to be rejected and supported the evidence that, in the short term, air temperature is the main limiting factor for xylogenesis in trees of these environments.
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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 | 0.001 | 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".