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Record W3217491215 · doi:10.1111/jne.13069

A half century of fish gonadotropin‐releasing hormones: Breaking paradigms

2021· review· en· W3217491215 on OpenAlexafffund
Yonathan Zohar, Nilli Zmora, Vance L. Trudeau, José Antonio Muñoz‐Cueto, Matan Golan

Bibliographic record

VenueJournal of Neuroendocrinology · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReproductive biology and impacts on aquatic species
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaMinisterio de Ciencia e InnovaciónUniversity of OttawaAgricultural Research Development AgencyNational Science Foundation
KeywordsFish <Actinopterygii>BiologyGonadotropin-releasing hormoneNeuroendocrinologyHormoneGonadotropinInternal medicineEndocrinologyFisheryMedicineLuteinizing hormone

Abstract

fetched live from OpenAlex

The field of fish gonadotropin-releasing hormones (GnRHs) is also celebrating its 50th anniversary this year. This review provides a chronological history of fish GnRH biology over the past five decades. It demonstrates how discoveries in fish regarding GnRH and GnRH receptor multiplicity, dynamic interactions between GnRH neurons, and additional neuroendocrine factors acting alongside GnRH, amongst others, have driven a paradigm shift in our understanding of GnRH systems and functions in vertebrates, including mammals. The role of technological innovations in enabling scientific discoveries is portrayed, as well as how fundamental research in fish GnRH led to translational outcomes in aquaculture. The interchange between fish and mammalian GnRH research is discussed, as is the value and utility of using fish models for advancing GnRH biology. Current challenges and future perspectives are presented, with the hope of expanding the dialogue and collaborations within the neuroendocrinology scientific community at large, capitalizing on diversifying model animals and the use of comparative strategies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.330
Teacher spread0.287 · 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
GenreReview

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

Citations43
Published2021
Admission routes2
Has abstractyes

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