Rocks, lichens, and woody litter influenced the soil invertebrate density in upland tundra heath
Bibliographic record
Abstract
Soil invertebrates are an integral part of Arctic ecosystems through their roles in the breakdown of litter, soil formation, and nutrient cycling. However, studies examining soil invertebrates in the Arctic are limited and our understanding of the abiotic and biotic drivers of these invertebrate communities remains understudied. We examined differences in soil invertebrate taxa (mites, collembolans, enchytraeids) among several undisturbed upland tundra heath sites in Nunavut Canada and identified the drivers (vegetation and substrate cover, soil nutrients and pH) of the soil invertebrate community across these sites. Soil invertebrate densities were similar to that of other Arctic studies. While invertebrate communities were relatively consistent between our sites, cover of rocks, woody litter, and the lichen Alectoria nigricans had significant, positive influences on the density of all invertebrates studied. Mites and collembolans were more closely associated with cover of lichens, whereas enchytraeids were more closely associated with woody litter and rocks. Our results suggest that anthropogenic (e.g., resource exploration and extraction) and/or natural (e.g., climate change) disturbances that result in changes to the vegetation community and woody litter inputs will likely impact soil invertebrates and the ecosystem services they provide.
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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 | 0.001 | 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".