Long term effect of fire severity on carabid and lichen assemblages
Bibliographic record
Abstract
Variation in fire severity strongly influences post-fire forest development. The resulting fine scale forest heterogeneity could impact biodiversity over long periods after fire events. In the James Bay area of northern Québec, differential seed mortality caused by high and low crown fire severity resulted in dramatic variation in regeneration density of both jack pine and black spruce. Sixty years after such fire event, we show that composition of lichen and carabid assemblages varied significantly between areas of high (c. 2600 stems/ha) and low (c. 560 stems/ha) stem density established by differential crown fire severity. The carabids, Notiophilus semistriatus and Miscodera arctica, were found in low stem density areas while Carabus taedatus and Pterostichus brevicornis were found in high stem density areas. Amount of bare ground was higher in low stem density area which may favor active visual diurnal hunters such as Notiophilus species. Cladonia rangiferina and C. stellaris were associated with high stem density area while C. uncialis and C. mitis were associated with low stem density area. This likely reflects the fact that photosynthetic rate of C. rangiferina is optimal under shady areas whereas C. uncialis is better adapted to hot, dry and sunny conditions. Thus, variation in fire behavior led to long-lasting variation in forest conditions that clearly affected both lichen and carabid assemblages even 60 years after fire.
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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 | 0.000 | 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".