Bibliographic record
Abstract
In domestic avian species, eggs are stored at cool temperatures until they can be placed into an incubator. The low temperature-induced diapause enables the embryo to survive until optimal temperature and humidity incubation conditions can be provided to support embryonic growth. Egg storage is a logistical necessity for the hatching egg industry both at the breeder farm and at the hatchery. However, it is well known that egg storage longer than 7 d negatively influences hatchability. At the cellular level, long-term egg storage induces cell death. This appears to be occurring both via necrosis and apoptosis. The result is higher embryonic mortality and, consequently, lower hatchability. In addition, long-term egg storage influences embryonic development and metabolism. Embryos of eggs stored long-term can be affected such that they do not initiate growth after proper incubation temperatures are provided; they initiate growth, but grow at a slower rate than eggs stored short term; and they are affected in both of the previously mentioned ways. Development of equipment to measure the embryonic metabolism of individually incubating eggs over the entire 21 d of incubation has provided further evidence that embryo metabolism has changed due to storage. One of the methods to reduce the negative effects of long-term storage has been to incubate eggs for short periods before storage. In both turkey and chicken eggs, this technique has been successful in improving the hatchability of long-term stored eggs. It is hypothesized that particular embryonic developmental stages are better able to survive long-term storage. Future research should focus on the mechanisms behind this improved ability to survive storage.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".