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Insights into the effects of vibration on skeletal development and growth in teleosts

2022· article· en· W4225403207 on OpenAlexafffund
Tamara A. Franz‐Odendaal, Shirine Jeradi

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsMount Saint Vincent University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyEvolutionary biologyNeuroscience

Abstract

fetched live from OpenAlex

Teleosts are superb models to study development and growth because their entire life history can be studied in a laboratory setting. The objective of this research is to understand the impacts of the external environment, namely whole body low frequency vibrations on skeletal development. We exposed zebrafish larvae at different stages of development to low frequency vibrations by placing them on a custom built vibration instrument for four days. Our results show age‐related and bone‐type specific effects. For example, some cartilages of the caudal fin were affected while others were not. This data suggests that chondrogenic cell progenitors are capable of sensing and reacting to mechanical stimuli early during development. Furthermore, we show that this effect is sox9‐independent. In summary, this data shows that there is a critical window during early caudal fin development that is susceptible to environmental influence. The plasticity of the skeleton to respond to and adapt to external influence is remarkable. These responses often have lasting impacts on the resulting phenotype and provides a hint at how development influences evolution.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 head0.008
GPT teacher head0.203
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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
Published2022
Admission routes2
Has abstractyes

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