Bibliographic record
Abstract
Newborn piglets have a high incidence of preweaning mortality that is not only associated with low birth weights but also with the presence of intra-uterine growth-restricted (IUGR) piglets. Such IUGR piglets are commonly seen in litters from hyperprolific sows as a result of insufficient placental transfer of nutrients. Nutritional strategies can be used prior to and during gestation to enhance foetal development and can also be implemented in the transition period to reduce the duration of farrowing and increase colostrum yield. Recent findings showed that the energy status of sows at the onset of farrowing is crucial to diminish stillbirth rate. Newborn piglets often fail to consume enough colostrum to promote thermostability and subsequent growth, and this is particularly problematic in very large litters when there are fewer available teats than the number of suckling piglets. One injection of 75 IU of oxytocin approximately 14 h after farrowing can prolong the colostral phase, hence increasing the supply of immunoglobulins to piglets. Nevertheless, assistance must be provided to piglets after birth in order to increase their chance of survival. Various approaches can be used, such as: (1) optimising the farrowing environment, (2) supervising farrowing and assisting newborn piglets, (3) using cross-fostering techniques, (4) providing nurse sows, and 5) providing artificial milk. Although research advances have been made in developing feeding and management strategies for sows that increase performance of their newborn piglets, much work still remains to be done to ensure that maximal outcomes are achieved.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".