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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".