Comparative costs of programmes to conserve chicken genetic variation based on maintaining living populations or storing cryopreserved material
Bibliographic record
Abstract
1. There have been substantial losses of chicken lines kept for research in recent years and the objective of this research was to critically review alternative methods of preserving genetic resources. 2. The costs of programmes using living populations, semen cryopreservation and reconstitution, and ovary and semen cryopreservation and reconstitution were evaluated over 20 years using biological parameters of cryopreservation and population reconstitution that were derived from the literature. 3. Keeping live populations was most cost effective for periods of up to three years, but keeping live populations is increasingly difficult to justify with longer periods and any research population that will not be used within five years should be cryoconserved and in situ maintenance discontinued. 4. The rapid reconstitution possible using ovaries and semen would allow the inclusion of cryopreserved material in a short-term research project with the cost of recovery included in the budget. The low cost of cryoconservation suggests that all avian material should be conserved and reconstituted when needed for research.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".