Impact of slash loading on soil temperatures and aspen regeneration
Bibliographic record
Abstract
Natural regeneration is used to restock trembling aspen (Populus tremuloides Michx.) cutblocks and factors controlling regeneration are areas of interest and concern to the forest industry. Harvest operations in Manitoba require that coarse woody debris, or slash, be left and distributed in cutblocks. The objective of this study was to investigate the effects of slash loading on soil temperatures and aspen regeneration, and implications for harvest operations in the Duck Mountain area. Early sucker growth, initiation, and soil temperatures were surveyed in six winter and six summer cutblocks under different levels of slash loadings. A growth chamber study, using field temperature data as a guideline, examined the effects of diurnal temperature variation on sucker initiation and production. In winter and summer cutblocks, mean depths to sucker initiation from the parent root were 4.6 + 2.4 cm and 3.4 + 2.1 cm, respectively, and initiation of suckers occurred mainly from parental roots located in the LFH layer. Daily mean soil temperatures during the growing season were significantly lower under higher levels of slash (difference of 3.6 oC during May). Higher amounts of slash also significantly shortened the length of the growing season (89 fewer days above 0 oC in one season) and decreased the number of suckers produced (150 000 ha–1 decreased to 14 000 ha-1), sucker volume (decreased by 256 cm3m-2) and leaf area index (decreased by 0.9). There was no difference in sucker production between any diurnal temperature treatments in the growth chamber study. Shallow depth to sucker initiation has important implications for harvest operations using heavy machinery especially those occurring during the summer season. Moderate levels of slash in summer cutblocks, and heavy levels of slash in winter cutblocks limit sucker growth. Although slash decreases diurnal temperature amplitudes, this may not be the reason for the decrease in sucker production associated with increased levels of slash. Both soil temperature and early sucker growth are strongly affected by slash loading; by monitoring harvest operations and the distribution of slash within cutblocks, the negative effect of heavy machine traffic and heavy piles of slash can be reduced and ensure successful forest regeneration.
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".