In vivo imaging in the rabbit as a model for the study of ovulation-inducing factors
Bibliographic record
Abstract
The study of factors responsible for eliciting ovulation in rabbits has been hampered by the lack of a suitable method of monitoring the ovaries in vivo. Ovarian imaging by ultrasound biomicroscopy was used in two experiments designed to determine the effects of seminal plasma on the ovulatory response in rabbits. In Experiment 1, female rabbits were group-housed and treated intramuscularly with saline, gonadotropin releasing hormone (GnRH), or seminal plasma of llamas or rabbits (n = 4 to 6 per group). Rabbits were euthanized eight days later to evaluate the ovarian response by ultrasound biomicroscopy ex situ. No differences among groups were detected in the proportion of rabbits that ovulated or in the number and size of corpora lutea. The high incidence of ovulation in the negative control group was unexpected, and confounded determination of an ovulation-inducing effect of seminal plasma. In Experiment 2, female rabbits were caged individually, and treated as in Experiment 1 (n = 5 to 7 per group). The ovarian response was evaluated in vivo by transcutaneous ultrasound biomicroscopy. Ovulation and formation of corpora lutea were detected only in rabbits given GnRH. A preovulatory surge in plasma luteinizing hormone concentration and a post-ovulatory rise in plasma progesterone concentration were detected only in rabbits treated with GnRH. Surgical translocation of the ovaries to a subcutaneous position enabled longitudinal assessment of the ovulatory response by ultrasound biomicroscopy. Results clearly documented the effect of physical/social interaction on ovulation in rabbits, and did not support the hypothesis that seminal plasma elicits ovulation in rabbits.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".