Effects of mechanized careful logging on natural regeneration and vegetation competition in the southeastern Canadian boreal forest
Bibliographic record
Abstract
Careful logging regulations in Quebec restrict circulation of harvesting and forwarding or skidding machinery to evenly spaced, parallel trails, which creates a particular pattern of disturbed and relatively undisturbed zones in cutovers. A 7-year monitoring study was established to evaluate the effects of careful logging on vegetation development in the southern boreal forest of Quebec. A total of 255 sample plots (2 m 2 ) were located in seven cutovers in predominantly black spruce (Picea mariana (Mill.) BSP) forests that were whole-tree "careful logged": 120 on fresh to moist silty clays or silty clay loams and 135 on dry to fresh loamy sands. Three microsites were sampled: skid trails and the edge and the centre of protection strips. A gradient of disturbance from the skid trail to centre of the protection strip was evident for finer textured sites. Careful logging resulted in high densities of black spruce and balsam fir (Abies balsamea (L.) Mill.) (> 20 000 stems/ha each) in the protection strip. Survival of other understory species was also favoured in protection strips. Higher disturbance levels in skid trails favoured establishment of larch (Larix laricina (Du Roi) K. Koch), raspberry (Rubus idaeus L.), and graminoids. Reduction of ericaceous cover occurred in skid trails on coarse-textured sites but was only temporary. Softwood stocking 7 years after harvest (based on 2-m 2 plots), ranged from 69 to 74% on fine- to medium-textured sites and from 31 to 51% on coarse-textured sites. The pattern of vegetation development created by careful logging has important implications for silvicultural decisions and stand modelling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".