Short-term impacts of logging on understorey vegetation in a jarrah forest
Bibliographic record
Abstract
Summary In 1985, new silvicultural treatments were implemented in jarrah (Eucalyptus marginata) forests available for wood production. As part of a scientific investigation into the ecological impacts of two of these treatments, gap cutting and shelterwood cutting, a survey was conducted 4 years after logging to examine the effects of these treatments on understorey vegetation species richness and abundance. Sampling scale was found to be an important factor affecting the results and subsequent interpretation of impacts. At the coupe scale, native plant species richness in unlogged coupe buffers was similar to that in adjacent logged patches. However, the mean number of species per 1 m2 was 20%-30% higher in the unlogged buffers than the logged patches. At all sampling scales, the abundance (number of individual plants) of native plants was 20%-35% higher in the buffers, but the abundance of introduced (weed) species was significantly higher in the logged patches. The abundance of weeds, which are mostly annual grasses and short-lived herbs, is likely to diminish with time. The time to recovery of native species abundance and the ecological significance of this is uncertain. Given the reported low seedling regeneration rate and limited dispersal capacity of many woody shrubs and perennial herbs, they are unlikely to return to pre-logging levels in the medium term. We attribute the reduction in the abundance of native plants mainly to mechanical soil disturbance, which ranged from 60% to 80% of the area of logged coupes, physical damage to the vegetation associated with logging and to intense heating of the topsoil during the post-logging silvicultural burn. Recommendations are made for reducing the negative impacts of logging operations on the understorey.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".