Contrasting effects of season and method of harvest on soil properties and the growth of black spruce regeneration in the boreal forested peatlands of eastern Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<ja:p>It has been suggested that without sufficient soil disturbance, harvest in boreal forested peatlands may accelerate paludification and reduce forest productivity. The objectives of this study were to compare the effects of harvest methods (clearcutting vs. careful logging) and season (summer vs. winter harvest) on black spruce regeneration and growth in boreal forested peatlands of eastern Canada, and to identify the soil variables that favour tree growth following harvest. Moreover, we sought to determine how stand growth following harvest compared with that observed following fire. The average tree height of summer clearcuts was greater than that of summer carefully logged stands and that of all winter harvested sites. Summer clearcutting also resulted in a higher density of trees > 3 m and > 4 m tall and in a 50% reduction in Rhododendron groenlandicum cover, a species associated with reduced black spruce growth. Height growth of sample trees was related to foliar N and P concentrations, and to soil total N, pH and available Ca and Mg but not to harvest method or season. Our results also indicate that summer clearcutting could produce stand productivity levels comparable to those observed after high-severity soil burns. These results suggest that summer clearcutting could be used to restore forest productivity following harvest in forested peatlands, and offer further support to the idea that sufficient levels of soil disturbance may be required to restore productivity in ecosystems undergoing paludification.</ja:p>
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.
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 | 0.001 | 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) | 0.000 | 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 it