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Record W2082689464 · doi:10.7557/2.31.2.2005

Development and survival of Ostertagia gruehneri under natural and artificially warmed conditions on the Canadian tundra

2011· article· en· W2082689464 on OpenAlexaffabout
Bryanne M. Hoar, Susan Kutz

Bibliographic record

VenueRangifer · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTundraClimate changeEcologyEcosystemOstertagiaTrophic levelEnvironmental scienceArcticBiologyGeographyFeces

Abstract

fetched live from OpenAlex

Climate change in the Arctic is occurring at an unprecedented rate and is anticipated to alter the ecology of northern ecosystems, including the patterns, diversity, and transmission of infectious diseases. Ostertagia gruehneri is the most common gastrointestinal nematode in caribou and can cause decreased food intake, weight loss, and reduced pregnancy rates in Rangifer species. Because O. gruehneri has a direct life-cycle that includes a free-living stage, the develop ment and survival rates of this parasite are influenced by climate and climate change. To investigate the response of the free-living stages of O. gruehneri to climate change field experiments were done from May to September 2007-08 at the Tundra Ecosystem Research Station (TERS), Daring Lake, Northwest Territories. Fecal plots containing 0. gruehneri were established on the tundra under natural and artificially warmed conditions. Plots were sampled throughout the summer to determine development and survival rates of O. gruehneri and to compare between the two climate regimes (natural vs. warmed). Effects of both temperature and relative humidity on development and survival were investigated. Results from these field seasons will be used, together with laboratory experiments, to develop and validate a predictive model for the impacts of climate change on the epidemiology of O. gruehneri.

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 score0.102
Threshold uncertainty score0.996

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 studies0.0000.001
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 head0.035
GPT teacher head0.204
Teacher spread0.169 · 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

Citations0
Published2011
Admission routes2
Has abstractyes

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