Technologies Related with the Artificial Insemination in Buffalo
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In buffalo oestrus behaviour has a lower intensity than in cows and is much more difficult to detect, limiting the application of artificial insemination (AI) program. Several methods of heat detection have been developed for use in cattle; these include visual observation, heat mount detectors, tail paint, chin-ball markers, teaser animals and electronic devices. In buffalo, unlike cattle, the female are receptive to mounting activity mainly by the bull and occasionally by other cows. Consequently unless a buffalo bull is to be left running with the herd it can be difficult to know when oestrus is occurring. The presence of a teaser bull is helpful to identify buffaloes in heat; in this case the standing oestrus is the most reliable sign referable to a next ovulation. Other heat detection aids utilized in buffalo include: pedometers; vaginal probes; pressure sensitive telemetry device (Heat Watch®). In order to increase the use of AI easy management schemes, that not require the identification of oestrus, have been studied. These schemes are based on the manipulation of the hormonal events occurring during the oestrous cycle as: manipulate peripheral progesterone concentration (by PGF2a or progesterone releasing device); manipulate follicular growth and timing of ovulation (by GnRH and PGF2a). A brief description of these technologies, with special reference to synchronization protocols to apply fixed time AI in buffalo, are presented in this review. The potential application of predetermining the sex of offspring will be also discussed, with reference to the techniques available for commercial practice in buffalo.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it