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Record W4414519668 · doi:10.3390/fishes10100478

Validation of Using Multiplex PCR with Sex Markers SSM4 and ALLWSex2 in Long-Term Stored Blood Samples to Determine Sex of the North American Shortnose Sturgeon (Acipenser brevirostrum)

2025· article· en· W4414519668 on OpenAlexaff
Hajar Sadat Tabatabaei Pozveh, Salar Dorafshan, Tillmann J. Benfey, Jason A. Addison, Matthew K. Litvak

Bibliographic record

VenueFishes · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMount Allison UniversityUniversity of New Brunswick
Fundersnot available
KeywordsSexingMultiplexAcipenserMultiplex polymerase chain reactionPolymerase chain reaction

Abstract

fetched live from OpenAlex

Sex-specific information is crucial for sturgeon culture, conservation, and fisheries management. However, identifying their sex is difficult outside the spawning season. Two recently identified female-specific loci (AllWSex2 and SSM4) are conserved across many Acipenserid species, but they have not been validated for all species within this family. This study aimed to (1) determine whether SSM4 can be used to sex shortnose sturgeon, (2) develop and test a multiplex PCR technique using both ALLWSex2 and SSM4 for sexing shortnose sturgeon, (3) determine if long-term stored blood samples can be used to sex shortnose sturgeon, and (4) test the effect of storage temperature on DNA degradation. DNA was extracted from frozen RBC samples from 36 previously sexed fish. A multiplex PCR was set up using three pairs of primers: AllWSex2 and SSM4, as female-specific loci, and mtDNA, as an internal control. AllWSex2 and SSM4 allowed for perfect discrimination of sex. While long-term storage and storage temperature did cause DNA degradation, the signal was still strong enough after 8 years of cold storage for reliable sex determination. This suggests that researchers now have the ability to re-examine archived/frozen samples to determine the sex of their sturgeon.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.986

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 head0.015
GPT teacher head0.230
Teacher spread0.216 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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