Effects of jumping worms on European earthworms \nand soil properties
Bibliographic record
Abstract
Earthworms are ecosystem engineers which alter soil structure and impact other organisms and ecosystem functioning.In 2014, pheretimoid "jumping worms" (Megascolecidae spp.) were discovered in Ontario, Canada, with later discoveries in New Brunswick (2021), and Nova Scotia (2022).Jumping worms are having substantial impacts in the northeastern United States, including effects on nutrient cycling and other soil organisms.In Canada, little research has been done to examine spread or effects of jumping worms since they have established only recently.Thus, we sampled at a residential property in Oromocto, New Brunswick, which was the first location where jumping worms were found in the province.Our objectives were to evaluate: (1) how jumping worms impact soil properties (i.e., nitrogen, carbon); (2) how their presence impacts the abundance of European earthworms; and (3) the effectiveness of two jumping worm sampling methods.We found that jumping worms did not have significant impacts on European earthworm species or soil carbon, but they did have significant impacts on soil nitrogen levels.Our results suggest the existence of a positive relationship between jumping worm abundance and soil nitrogen levels when jumping worm abundance is low.Also, both sampling methods (i.e., mustard solution and wooden discs) were equally effective at detecting the presence of jumping worms at a site.Over the longer term, we hope to track the expansion of this population in order to determine rates of spread.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".