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Fecal counts of lungworm larvae and reproductive effort in bighorn sheep, <i>Ovis canadensis</i>

2005· article· en· W2163834455 on OpenAlexfundno aff
Fanie Pelletier, Karen Ann Page, Timothée Ostiguy, Marco Festa‐Bianchet

Bibliographic record

VenueOikos · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversité de SherbrookeAlberta Conservation Association
KeywordsOvis canadensisBiologyLungwormReproductionOvisDomestic sheep reproductionFecesAnimal scienceCorriedaleZoologyParasite hostingLarvaVeterinary medicineEcologyDemographyPopulation

Abstract

fetched live from OpenAlex

Because parasite resistance and reproduction require metabolic resources, life‐history models predict a tradeoff between current reproduction and parasite load. These tradeoffs have been widely studied in birds, but few studies have been conducted on mammals. We monitored lungworm ( Protostrongylus spp.) larvae counts in bighorn sheep ( Ovis canadensis ) over four years to examine how individual differences in fecal output of lungworm larvae (LPG) by yearlings and adults were affected by season, sex, body mass, age and reproductive effort. We also compared lamb mass at six months and LPG. Overall, we found that LPG varies seasonally, peaking in females prior to lambing and in males during the rut. Age had no effect on LPG for either sex. During autumn, we found no effect of age or mass on LPG for sheep one year and older. Lamb body size or sex did not affect LPG. Females that weaned a lamb had higher counts than females that did not produce a lamb or females whose lamb died during summer. For rams, social rank and testosterone levels were not related to LPG but LPG increased with time spent searching for estrous ewes during the rut. Our results suggest a tradeoff between parasite resistance and reproductive effort in bighorn sheep of both sexes.

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 score0.018
Threshold uncertainty score0.811

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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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

Citations60
Published2005
Admission routes1
Has abstractyes

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