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Record W2811377159 · doi:10.1016/j.heliyon.2018.e00668

Identification and characterization of microRNAs from the tube foot in the sea urchin Strongylocentrotus intermedius

2018· article· en· W2811377159 on OpenAlexaff
Yaoyao Zhan, Yingying Li, Dongyao Cui, Qiantong Pei, Jingxian Sun, Weijie Zhang, Yaqing Chang

Bibliographic record

VenueHeliyon · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEchinoderm biology and ecology
Canadian institutionsDalhousie University
FundersNational Natural Science Foundation of China
KeywordsSea urchinBiologyIdentification (biology)FisheryFish <Actinopterygii>EcologymicroRNAZoologyGeneGenetics

Abstract

fetched live from OpenAlex

MicroRNAs (miRNAs) play critical roles in regulating many bio-processes of eukaryotes. The sea urchin Strongylocentrotus intermedius (an important fishery resource) is of great economic importance in Japan, North Korea, Russia, and China. In the current study, miRNAs of tube foot in S. intermedius were firstly identified and characterized. Data in this study can provide more genomic information for the further understanding of the complex regulation network in sea urchins and present a new way for monitoring the health status of cultured sea urchins.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.668
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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