MétaCan
Menu
Back to cohort
Record W2136800986 · doi:10.4039/n11-028

Relationships between gregarine infection in damselflies, wetland type, and landscape characteristics

2011· article· en· W2136800986 on OpenAlexaffabout
Julia J. Mlynarek, Daniel G. Bert, G. Haydeé Peralta-Vázquez, Joanna A. James, Mark R. Forbes

Bibliographic record

VenueThe Canadian Entomologist · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsCarleton University
Fundersnot available
KeywordsWetlandOdonataDamselflyEcologyHabitatCoenagrionidaeInvertebrateLandscape ecologyGeographyBiology

Abstract

fetched live from OpenAlex

Abstract Although human-modified landscapes are characterized by the loss of natural habitats, new habitats also can be created and exploited by many species. The importance of landscape change to invertebrate associations (particularly host-parasite associations) is understudied. Our objective was to determine whether prevalence and intensity of gregarine parasitism in the damselfly Ischnura verticalis (Say) (Odonata: Coenagrionidae) differed between 17 artificial and 7 natural wetlands in landscapes that varied in amount of forest and wetland cover and road density determined at spatial extents of 500m and 1km from each wetland. Wetlands were located in and around Ottawa, Ontario, and Gatineau, Quebec, Canada. Wetland type did not account for significant variation in principal components based on forest and wetland cover and road density at either spatial extent. Gregarine prevalence was higher in damselflies collected from natural wetlands than in those collected from artificial wetlands and was positively associated with increasing forest cover. In contrast, gregarine intensity was inversely related to road density. Our results suggest that parasitism of damselflies by gregarines is associated with wetland type and landscape characteristics, although the mechanisms producing such relationships are unknown.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.062
GPT teacher head0.286
Teacher spread0.223 · 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

Citations13
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueThe Canadian EntomologistSame topicParasite Biology and Host InteractionsFrench-language works237,207