Effects of a recent wildfire and clearcuts on ground-dwelling boreal forest spider assemblages
Bibliographic record
Abstract
Ground-dwelling spider (Araneae) assemblages were compared between recent clearcuts and burned black spruce (Picea mariana (Mill.) BSP) forests. Spiders were sampled using pitfall traps placed in 6 recently burned sites, 6 recently clear-cut sites, and 6 undisturbed sites in eastern Quebec, Canada. A total of 2935 individuals from 95 spider species were identified. Catch rates for hunting spiders were significantly higher in the clear-cut stands than in the burned stands, but between-stand species turnover of hunting spiders was twice as high in the burned stands as in the clear-cut stands. Web-building spiders had similar catch rates in the three stand types, but had the highest species turnover and gamma diversity in the undisturbed sites. Correspondence analysis showed that the composition of the spider assemblages varied among the three types of stands. Spider assemblages found in clearcuts were associated with environmental variables reflecting dry, open, disturbed forest floor, while assemblages found in burned stands were correlated with high percentages of shrub cover and dried mosslichen substrate and deeper litter. We conclude that ground-living spider assemblages in boreal forest respond very differently to clearcuts and fires and we suggest modifications to present forestry practices to create disturbances that are more similar to wildfires.
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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 | 0.000 | 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".