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LHRHa-induced ovulation of the endangered-Caspian brown trout (Salmo trutta caspius) and its effect on egg quality and two sex steroids: testosterone and 17α-hydroxyprogestrone

2009· article· en· W1919225053 on OpenAlexaff
Ahmad Noori, Bagher Mojazi Amiri, Alireza Mirvaghefi, D. W. Baker

Bibliographic record

VenueAquaculture Research · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReproductive biology and impacts on aquatic species
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiologyBrown troutSalmoOvulationTestosterone (patch)EndocrinologyInternal medicineMethyltestosteroneHuman fertilizationAnimal scienceReproductionHormoneFish <Actinopterygii>FisheryAnatomyEcologyMedicine

Abstract

fetched live from OpenAlex

To induce synchronized ovulation, migrating wild Caspian brown trout (Salmo trutta caspius) females were treated with two interperitoneal injections of Des-Gly10, d-Ala6 LHRH (LHRHa), given 3 days apart. Two injections of 100 μg kg−1 body weight of this hormone effectively induced ovulation. Within 27 days from the second injection, all fish injected with 100 μg kg−1 LHRHa had ovulated compared with 54.5% of the controls. The mean time to ovulation was reduced significantly (P<0.05) from 31.67±4.84 days in control fish and 28.83±7.31 days in sham-treated fish to 16.36±1.61 days in fish injected with 100 μg kg−1 LHRHa. The fertilization rate in 50 and 100 μg kg−1 LHRHa-injected fish was significantly lower than that in the control fish (P<0.05). In fish injected with 50 and 100 μg kg−1 LHRHa, significant (P<0.05) changes in testosterone (T) and 17α-hydroxyprogestrone (OHP) levels were observed. After the second LHRHa injection, the fish injected with 100 μg kg−1 showed the highest serum levels of testosterone and OHP. These results demonstrate that the use of LHRHa can effectively reduce the mean time to ovulation and induce synchronized ovulation in Caspian brown trout.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.066
GPT teacher head0.387
Teacher spread0.321 · 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 designBench or experimental
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

Citations9
Published2009
Admission routes1
Has abstractyes

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