Long-term impact of severe wildfire and post-wildfire salvage logging on macroinvertebrate assemblage structure in Alberta’s Rocky Mountains
Bibliographic record
Abstract
Wildfire is an important natural disturbance on forested landscapes influencing both physical and biological processes. The Lost Creek wildfire was one of the most severe on Alberta’s eastern slopes and provided a unique opportunity to assess the long-term impacts of wildfire and post-wildfire salvage logging on northern Rocky Mountain catchments. Macroinvertebrate sampling conducted 8 years after the wildfire indicated distinct macroinvertebrate assemblages in reference, burned, and burned and salvage-logged catchments. Reference catchments were characterised by more sensitive taxa (e.g. stoneflies) and had the lowest abundance of macroinvertebrates. Burned catchments had the greatest abundance of macroinvertebrates and were characterised by greater abundance of chironomids and caddisflies. Burned and salvage-logged catchments supported high numbers of riffle beetles and crane flies. The unique assemblage structures between catchments indicate different mechanisms drive the ecological response in wildfire-affected catchments. Resource availability was an important driver of assemblage structure in the more productive burned catchments and habitat quality was a dominant driver in the burned and salvage-logged catchments. Despite the legacy effects observed in this study, fire-affected catchments still supported sensitive taxa and functional macroinvertebrate assemblages.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".