Foreword: A perspective on the role of emerging technologies for the propagation of companion animals, non-domestic and endangered species
Bibliographic record
Abstract
Assisted reproductive technologies (ART) have been used successfully in humans, domestic and laboratory species for many years. In contrast, our limited knowledge of basic reproductive physiology has restricted the application of ART in companion animal, non-domestic and endangered species (CANDES). Although there are numerous benefits, and in some cases a necessity, for applying ART for the reproductive and genetic management of CANDES, the challenges encountered with even the most basic procedures have limited the rate of progress. In this foreword we discuss the status of conventional ART, such as artificial insemination and in vitro fertilisation, as well as their benefits and inherent difficulties when applied to CANDES. It is upon these techniques, and ultimately our knowledge of basic reproductive physiology, that the success of emerging technologies, such as those described in this special issue, are dependent for success.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".