MétaCan
Menu
Back to cohort
Record W3092687968 · doi:10.1530/biosciprocs.19.0002

Mechanisms affecting litter sex ratio and embryo quality

2019· article· en· W3092687968 on OpenAlexaff
G. Oliver, P. Vendramini

Bibliographic record

VenueBioscientifica Proceedings · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLitterSex ratioEmbryo qualityEmbryoQuality (philosophy)Environmental scienceBiologyDemographyEcologyFisheryEmbryogenesisPhysicsSociology

Abstract

fetched live from OpenAlex

Sex ratios that deviate from 1:1 have been observed in response to a number of stimuli. In this review we will discuss sex ratio biasing, and the evolutionary and molecular mechanisms thought to underlie this phenomena in mammals. The role of embryo quality will be discussed in relation to sex ratio modulation and epigenetic programing of the embryo. Sex ratio skewing has been studied in many species and several factors have been proposed as influencing secondary sex ratios (body condition, maternal dominance, nutrition and developmental asynchrony). In swine, maternal nutrition has repeatedly been shown to influence offspring sex ratios, while maternal dominance and body condition exhibit less consistent evidence supporting their influence. Based on current evidence, we hypothesize that sex ratio biasing is the result of sexual dimorphisms that result in sex specific differences in embryo quality, and these differences lead to sex specific embryonic loss. The mechanisms through which sex specific loss occurs are not fully understood, however sexual dimorphisms in metabolism, gene expression and epigenetic mechanisms during early embryo development suggest that sex ratio modulation might be mediated through these mechanisms. We hypothesize that there are a number of mechanisms for skewing sex ratios in mammals, and that specific mechanisms are elicited in response to specific stimuli.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.037
GPT teacher head0.310
Teacher spread0.272 · 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueBioscientifica ProceedingsSame topicDemographic Trends and Gender PreferencesFrench-language works237,207