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Record W6929456317 · doi:10.5061/dryad.5k5k0

Data from: Linking innate immunogenetic variation with phenotypic traits in a wild population of tree swallows, Tachycineta bicolor

2017· dataset· en· W6929456317 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2017
Typedataset
Languageen
FieldHealth Professions
TopicGlobal Health and Epidemiology
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsFledgePopulationGenetic variationPhenotypeHeritabilityPhenotypic traitGeneSingle-nucleotide polymorphismReproductive success

Abstract

fetched live from OpenAlex

Unravelling the genetic basis of phenotypic variation among individuals is an important step in our understanding of evolution. Recent studies of innate immune genes, such as β -defensins, revealed that these genes had high levels of polymorphism. However, researchers have yet to quantify the effects of such variability on immune responses and fitness-related traits in wild populations. In this study, we assessed how the variability at six avian β -defensin (AvBD) genes was linked to an immune function and reproductive success in adult tree swallows (Tachycineta bicolor). We investigated the links between genetic variations using single nucleotide polymorphisms at AvBD genes, immune function as the bacterial killing ability (BKA) and fledging success. We assessed how female immunogenetics were linked to the presence of eggshell bacteria in their clutches and hatching success. We found weak associations between the presence of AvBD genes, BKA and eggshell bacteria. Our results suggested that homozygosity at some loci may be advantageous for defence against bacteria. Variability at β -defensin genes was not related to either hatching or fledging success. BKA of parents was positively linked with fledging success. More studies are needed to assess whether or not β -defensin genes are significantly affecting fitness-related traits in wild populations.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.391
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0040.003
Research integrity0.0010.002
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 head0.084
GPT teacher head0.400
Teacher spread0.316 · 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.

Study designObservational
Domainnot available
GenreDataset

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
Published2017
Admission routes1
Has abstractyes

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