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Record W4390959213 · doi:10.1016/j.ejsobi.2023.103590

Testing the impacts of invasive jumping worms at their northern range limit

2024· article· en· W4390959213 on OpenAlexafffundabout
S E Bennett, Helen R. P. Phillips, Anne C. Dalziel, Lawrence R. Manzer, Erin K. Cameron

Bibliographic record

VenueEuropean Journal of Soil Biology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInvertebrate Taxonomy and Ecology
Canadian institutionsSaint Mary's University
FundersH2020 Marie Skłodowska-Curie ActionsHorizon 2020Natural Sciences and Engineering Research Council of CanadaFonds en Fiducie pour la Faune du Nouveau-Brunswick
KeywordsJumpingEcosystemEcologyHabitatEcosystem engineerAbundance (ecology)Range (aeronautics)Environmental scienceLumbricidaeBiomass (ecology)BiologyEarthworm

Abstract

fetched live from OpenAlex

Earthworms can act as ecosystem engineers by altering soil structure, which impacts other organisms and ecosystem functioning. Jumping worms (family Megascolecidae) originating in Asia have been spreading in North America, extending their northern range limits to Ontario, Canada in the last decade and to New Brunswick in 2021. At the northern limits of their current range, little research has been done to examine the effects of jumping worms in these new habitats since their recent establishment. Our objectives were to evaluate: (1) how jumping worms impact soil nitrogen and soil carbon; (2) how their presence impacts the abundance of non-native European earthworms (family Lumbricidae); and (3) whether two sampling methods (i.e., mustard solution and wooden discs) are equally effective at detecting jumping worms. We sampled a residential property in Oromocto, New Brunswick, which was the first location where jumping worms were found in the province. Jumping worms did not have significant impacts on the abundance and biomass of European earthworms or soil carbon content in the top 5 cm of the soil, but they did significantly affect soil nitrogen levels. Both sampling methods were equally effective at detecting the presence of jumping worms. Further research is needed in managed landscapes, urban areas, and forests to determine the ecosystem impacts and invasion dynamics of jumping worms in Canada as this invasion progresses.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.178

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.206
Teacher spread0.156 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2024
Admission routes3
Has abstractyes

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