Bibliographic record
Abstract
The control of disease in the sow herd helps prevent disease spread through other stages of production and attention to sow health status can be key to creating herd immunity and, possibly, to the elimination of production-limiting diseases. Sows provide passive immunity for suckling piglets. Ideally a strong healthy piglet will be weaned and not a weak, infected piglet that will pass disease to pen-mates in the nursery. High sow mortality and premature culling due to illness and injuries result in lowered herd productivity because sows are removed before they reach maximum productivity and are replaced by less productive gilts. Farrowing is a period when the sow is vulnerable to health issues, such as uterine infections and mastitis, and is more prone to systemic infections such as erysipelas. Major causes of sudden death in sows, include hemorrhage due to gastric ulcers and gastrointestinal torsions, as well as heat stress, pylonephritis and heart failure. Sows are generally immune to most endemic diseases present on the farm because of previous exposure but some pathogens, when newly introduced, can cause herd outbreaks of disease involving the sows. For example, porcine reproductive and respiratory syndrome virus and swine influenza virus can result in widespread sow illness and possibly sow mortality. More often the consequences of sows becoming infected with a swine pathogen result in the most serious losses occurring to the fetuses or the suckling piglets. Reproductive losses that occur with an infection of parvovirus cause heavy losses to embryos and fetuses but generally no signs of illness in the dam. Diseases that do cause illness to the sow during lactation may result in high piglet mortality because the sick sow may not provide sufficient milk to prevent starvation of her piglets. To maximize herd performance, sow health must be optimized.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.191 | 0.090 |
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".