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Record W4311715174 · doi:10.1038/s41597-022-01887-1

Comparative transcriptome profiles of four sexually size dimorphic fish

2022· article· en· W4311715174 on OpenAlexaff
Lifei Luo, Zisheng Xu, Danyang Li, Zhen Hu, Zexia Gao

Bibliographic record

VenueScientific Data · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMinistry of Agriculture
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsSexual dimorphismTranscriptomeBiologyFish <Actinopterygii>ZoologyEvolutionary biologyFisheryGeneticsGene expressionGene

Abstract

fetched live from OpenAlex

Sexual size dimorphism is widespread in fish species. Although sex growth differences in multiple species have been studied successively, the commonalities of regulatory mechanisms across sexually dimorphic species are unknown. In this study, we performed RNA-seq analysis of four representative fish (loach, half-smooth tongue sole, yellow catfish, and Nile tilapia) with significant growth differences between females and males. Clean reads were identified from four fish species, ranging from 45,718,052 to 57,733,120. Following comparison transcriptome analysis, there were 1,132 and 1,108, 1,290 and 1,102, 4,732 and 4,266, 748 and 192 differentially expressed genes (DEGs) in the brain and muscle of loach, half-smooth tongue sole, yellow catfish, and Nile tilapia, respectively. Furthermore, the expression levels were validated by quantitative real-time PCR (qRT-PCR). Comparative transcriptome profiles of four fish described here will provide fundamental information for further studies on the commonalities of sexually size dimorphic fish in regulating growth differences between females and males.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.000

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 head0.067
GPT teacher head0.272
Teacher spread0.206 · 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 designNot applicable
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

Citations18
Published2022
Admission routes1
Has abstractyes

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