Pre-commercial thinning in boreal mixedwoods increases temperature extremes without affecting extreme low soil moisture values
Bibliographic record
Abstract
Pre-commercial thinning has potential to mitigate the effect of drought stress on growth but likely removes protection from environmental temperature extremes. Processes driving growth after density management are poorly understood but important when applying thinning to stands that will grow under future warmer and drier conditions. Consequently, we evaluated microclimate and resource availability in operational scale pre-commercial thinning trials (treated and control) of young (19-year-old) boreal trembling aspen/white spruce mixedwoods in northern Alberta, Canada. Thinned stands in this study experienced more temperature extremes, both <0°C and >30°C, than unthinned stands as well as the same quantity of extreme low soil moisture values. However, lower tree density in thinned stands provided more available heat and higher average soil moisture, especially during dry periods in the year. Soil nutrient supply rates were not different between treatments, nor was soil moisture during wet periods, nor was soil temperature in the early and late parts of the growing season. Regeneration of broadleaf trees species in thinned stands was substantial. Overall, pre-commercial thinning caused both positive and negative changes to the tree-growing environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".