Role and efficiency of artificial insemination and genome resource banking
Bibliographic record
Abstract
INTRODUCTION Historically, the breeding of giant pandas in ex situ programmes has been difficult due to behavioural incompatibility and interanimal aggression. Because some individuals fail to mate naturally, the potential loss of valuable genes is a major concern to effective genetic management (see Chapter 21). Consistently successful artificial insemination (AI) would allow incorporating genetically valuable males with behavioural or physical anomalies into the gene pool. This strategy becomes even more powerful when used in the context of a genome resource bank (GRB), an organised repository of cryopreserved biomaterials (tissue, blood, DNA and sperm) (see Chapter 7). The use of sperm cryopreservation and AI allows the movement of genes among zoos and breeding centres without needing to transfer animals, which is both stressful and costly. ‘Assisted breeding’ refers to the tools and techniques associated with helping a pair of animals propagate, from AI to embryo transfer to cloning, among others (Howard, 1999; Pukazhenthi & Wildt, 2004). With the exception of AI, there is not much need for most other assisted-breeding techniques for the giant panda. As will be demonstrated here, AI is quite adequate for dealing with most cases of infertility or with helping to maintain adequate gene diversity in the captive population. In fact, the major breeding facilities, especially the China Conservation and Research Centre for the Giant Panda (hereafter referred to as the Wolong Breeding Centre) and the Chengdu Research Base of Giant Panda Breeding, routinely use AI to increase pregnancy success.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".