The effect of fire severity and salvage logging traffic on regeneration and early growth of aspen suckers in north-central Alberta
Bibliographic record
Abstract
Density and growth of trembling aspen (Populus tremuloides Michx.) were measured in the first two years following wildfire to determine the effects of: 1) fire severity and 2) salvage logging damage on sucker regeneration. Results indicate that stand leaf area was not affected by fire severity, although the greatest number of suckers was produced following high severity burns. In contrast, plots with the highest level of machine disturbance in the salvage-logging study had 60% fewer suckers compared to the non-trafficked plots. These suckers tended to be smaller and had less leaf area than the non-trafficked plots, resulting in a stand leaf area reduction of up to 75%. This suggests that salvage logging could have a negative impact on the future growth and productivity of regenerating aspen stands. Key words: trembling aspen, regeneration, suckering, leaf area, wildfire, fire severity, salvage logging, machine traffic
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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 | 0.000 |
| 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 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".