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Record W2529354167 · doi:10.1017/s0031182016001748

Summer time predation on the obligatory off-host stage of an invasive ectoparasite

2016· article· en· W2529354167 on OpenAlexaff
Sirpa Kaunisto, ILKKA RAUNISMAA, Raine Kortet, Hannu Ylönen

Bibliographic record

VenueParasitology · 2016
Typearticle
Languageen
FieldMedicine
TopicDermatological diseases and infestations
Canadian institutionsWestern University
Fundersnot available
KeywordsBiologyPredationHost (biology)ZoologyEcologyStage (stratigraphy)

Abstract

fetched live from OpenAlex

Predation can regulate populations and strongly affect invasion success of novel prey. The deer ked (Lipoptena cervi; Linnaeus 1758) is an invasive ectoparasite of cervids that spends a long period of its life cycle outside the host. Prior to this study, virtually nothing was known about natural summer time predation on the deer ked. We aimed to evaluate the magnitude of summer time predation on L. cervi pupae in different habitats and to identify potential predators. We conducted a set of field experiments, where we exposed L. cervi pupae to various ground-dwelling vertebrate and invertebrate predators. The loss of pupae was monitored for different predator guilds. Three habitats of the moose, the main host species, were studied: (1) moist heath forest; (2) dry, logged heath forest; and (3) moist meadow. The results indicate notable summer time predation on L. cervi pupae, and the pupal predation varied within and between habitats, being lowest in the meadow habitat. We found a positive correlation between pupal loss and abundance of the common lizard (Zootoca vivipara), harvestmen (Opiliones), ground spiders (Gnaphosidae) and Formicinae-ants. We conclude that summer time predation during the pupal phase can have a notable local importance for the L. cervi abundance.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score1.000

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)0.0020.001

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.

Opus teacher head0.031
GPT teacher head0.333
Teacher spread0.302 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2016
Admission routes1
Has abstractyes

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