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Record W2162346107 · doi:10.1556/avet.58.2010.1.12

Plasma progesterone profiles in Beagle bitches with and without the whelping experience

2010· article· en· W2162346107 on OpenAlexaboutno aff
Makoto Seki, Norio Watanabe, Kenyo Ishii, Yoh-ichi Kinoshita, Takehiro Aihara, Shuji Takeiri, Takeshige Otoi

Bibliographic record

VenueActa Veterinaria Hungarica · 2010
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsBeagleMedicineMatingLabrador RetrieverInternal medicineEndocrinologyPhysiologyAndrologyGynecologyBiologySurgery

Abstract

fetched live from OpenAlex

The aim of this study was to investigate differences between the progesterone profiles of Beagle bitches with (multiparous) and without (nulliparous) the whelping experience and to examine whether the selection of bitches by progesterone analyses before the programmed mating improved the whelping rates. In the first experiment, the progesterone profiles of nulliparous and multiparous bitches were evaluated from Days 1 to 13 (onset of prooestrus = Day 0). The mean duration of the elevation in progesterone levels (> 2 ng/mL) after the onset of prooestrus tended to be approximately 1 day shorter in nulliparous bitches (7.7 days) than in multiparous bitches (8.5 days). In the second experiment, progesterone analyses in the bitches were carried out once on Days 4, 5 or 6. Bitches with progesterone levels of > 10 ng/mL were excluded from mating because it was unclear when the progesterone levels reached > 10 ng/mL considering the optimal date for mating. No significant differences were observed in the percentages of bitches excluded from mating and in the whelping rates of the mated bitches between the groups, irrespective of the day of progesterone analysis and the type of bitch. In conclusion, the initial elevation of progesterone levels was of shorter duration in nulliparous bitches. The selection of bitches by the measurement of progesterone levels once before mating was not effective for the programmed mating.

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 score0.743
Threshold uncertainty score0.981

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 integrity0.0000.001
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 head0.055
GPT teacher head0.311
Teacher spread0.256 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

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