Partial Harvest in Paludified Black Spruce Stand: Short-Term Effects on Water Table and Variation in Stem Diameter
Bibliographic record
Abstract
The boreal forest is considered to be a low productivity forest due to its cold climate and poorly drained soils promoting paludification. These factors create conditions favouring accumulation of undecomposed organic matter, which causes declining growth rates of forest stands, ultimately converting mature stands into peatlands. Under these conditions, careful logging is conducted during winter, which minimizes soil disturbance in northwestern Quebec boreal forest. This results in water table rise, increased light availability and paludification. Our main objective was to evaluate the short-term effect of partial harvesting as an alternative method to careful logging in winter to mitigate water table rise on black spruce (Picea mariana [Mill.] B.S.P.) stands. We quantified tree stem diameter variation and daily variation in water table depth in mature spruce stands before and after partial harvest (basal area reduction of 40%) and girdling (same basal area reduction with delayed mortality) during 2016 and 2017 growing seasons. Water table variation prior to and following silvicultural treatments did not differ one year after treatment. Daily stem diameter variation in black spruce did not differ between treatments and control. Furthermore, temperature exerted a positive effect on variation in water table and on stem diameter. These results suggest that partial harvest could be more effective than clearcutting to mitigate negative effects of a high water table while limiting paludification.
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".