Non-native invasive earthworms as agents of change in northern temperate forests
Bibliographic record
Abstract
Exotic earthworms from Europe and Asia are invading many northern forests in North America that currently lack native earthworms, providing an opportunity to assess the role of this important group of invertebrates in forest ecosystems. Research on earthworm invasions has focused on changes in soil structure and carbon (C) and nitrogen (N) cycling that occur following invasion. These changes include the mixing of organic and mineral soil horizons, decreases in soil C storage, and equivocal effects on N cycling. Less well studied are changes in the soil foodwebs that accompany earthworm invasion. Soils of north temperate forests harbor a tremendous diversity of microorganisms and invertebrates, whose distribution and abundance can be substantially altered by earthworm invasion. Furthermore, invasive earthworms can affect understory plant communities, raising concerns over the loss of rare native herbs in some areas. The ecological consequences of earthworm invasion are mediated through physical, geochemical, and biological effects. These effects vary with different earthworm species, as well as with the characteristics of the site being invaded. Earthworm invasions may have important interactions with other rapid changes predicted for northern forests in the coming decades, including climate and land-use change, increased nutrient deposition, and other biological invasions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".