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Record W6929360179 · doi:10.5061/dryad.j348s75

Data from: Are there synergistic or antagonistic effects of multiple maternally-derived egg components (antibodies and testosterone) on offspring phenotype?

2019· dataset· en· W6929360179 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2019
Typedataset
Languageen
FieldMedicine
TopicAdrenal Hormones and Disorders
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsOffspringYolkMaternal effectHatchingSexual maturityLitterReproduction

Abstract

fetched live from OpenAlex

Eggs are multivariate in that they contain multiple maternally-derived egg components (e.g. hormones, antibodies, mRNA, antioxidants) which are thought to influence offspring phenotype. However, most studies have focused on single egg components and on short-term effects. Here, we simultaneously manipulated two egg components, maternally-derived antibodies (MAb) and yolk testosterone (T) to assess potential synergistic or antagonistic effects on offspring phenotype from hatching to sexual maturity. We found no evidence for short-or long-term effects of either MAb or yolk T alone, or their interaction, on hatching mass, size at fledging (tarsus), body mass at sexual maturity (day 82), chick survival, humoral immune function, or any measured female reproductive trait at sexual maturity. There was a positive effect of yolk T, but not MAb, on offspring PHA response at 26 days of age but at 82 days of age MAb, but not yolk T, had a positive effect on PHA response. There was also a MAbsex interaction on 30 day chick mass, and a positive effect of yolk T on male courtship behaviour at sexual maturity. However, we found no evidence for synergy, i.e. where offspring treated both with MAb and yolk T had higher trait values than offspring treated with either MAb or yolk T alone for any measured trait. Similarly, evidence for antagonistic (compensatory) effects, where offspring treated both with MAb and yolk T had intermediate trait values compared with offspring treated with either MAb or yolk T alone, was equivocal.

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 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 score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0000.000
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 head0.045
GPT teacher head0.285
Teacher spread0.240 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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