Precipitation and relative humidity favours tree growth while air temperature and relative humidity respectively drive winter stem shrinkage and expansion
Bibliographic record
Abstract
Forest ecosystems have a major role in sequestering atmospheric CO 2 and as such, their resilience is of upmost importance. In the boreal forest, trees grow only during a short period when air temperature is favourable. During winter, trees have specific mechanisms to survive in the cold air temperature. In order to understand the response of trees to a changing climate, this study assessed the influence of environmental variables on three phases of tree radial variation (i.e., growth, shrinkage and expansion) during three periods of the year (i.e., growing season, freeze–thaw period, and winter). The three phases were extracted from stem radial variation measured for as much as 11 years on 12 balsam fir [ Abies balsamea (L.) Mill.] trees located in a cold and humid boreal forest of eastern Canada. The random forest algorithm was used to model each phase during each period. Our results show that tree growth increased with high precipitation and high relative humidity. Stem shrinkage was affected mostly by solar radiation, precipitation and vapour pressure deficit during the growing season and was likely caused by tree transpiration. During both the freeze–thaw and winter season periods, stem shrinkage increased with decreasing air temperature. During the growing season, stem expansion was related to 1-day-lag solar radiation and 1-day-lag vapour pressure deficit, which are the same variables associated with shrinkage the preceding day. Stem expansion increased with increasing air temperature and relative humidity during the freeze–thaw and winter season periods, respectively. This study shows that sink-driven tree growth is promoted mostly under humid conditions while antecedent dry and warm conditions are required during the growing season for trees to assimilate carbon through photosynthesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".