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Record W7018676122

Dişi köpeklerde östrus siklusunun farklı dönemlerindeki serum Vitamin E ve malondialdehit düzeyleri ile bazı üreme özelliklerinin değerlendirilmesi (The Evaluation of Some Reproductive Parameters and Serum Concentrations of Vitamin E and Malondialdehyde in Different Stages of Estrus Cycles in Bitches)

2015· other· en· W7018676122 on OpenAlexaboutno aff

Bibliographic record

VenueDSpace - FIRAT (Fırat University) · 2015
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMalondialdehydeVitamin ELitterEstrous cycleSerum concentrationVitamin
DOInot available

Abstract

fetched live from OpenAlex

This study was performed to determine whether there is a relationship between some reproductive
\ntraits and the serum concentrations of vitamin E and malondialdehyde (MDA) in different stages of
\nestrus cycles in bitches. A total of 19 multiparous bitches (8 Labrador Retrievers and 11 German
\nShepherds) were used in this study. The onset of proestrus was designated as the first day of
\nsanguineous vaginal discharge. Following that, vaginal smears were obtained from each bitch daily
\nuntil onset of diestrus. Blood samples were collected in the onset of proestrus, estrus and diestrus
\nand, serum concentrations of vitamin E and MDA were determined. The bitches were naturally mated
\non day 2 and 4 after onset of estrus. There was no significant difference (P>0.05) in the serum
\nconcentrations of vitamin E and MDA in different stages of estrus cycles although there was individual
\ndifference among bitches. In addition, there was no relationship between litter size and other
\nreproductive traits and the serum concentrations of vitamin E and MDA. There was a negative
\ncorrelation (P<0.01 r=-0,829) between the length of gestation and litter size. In conclusion, we
\nobserved that the serum concentration of vitamin E and MDA was similar in different stage of cycles
\nand, they had no significant effect on the reproductive traits in bitches.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0030.002
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.030
GPT teacher head0.249
Teacher spread0.219 · 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 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
Published2015
Admission routes1
Has abstractyes

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