The ART of selecting the best embryo: A review of early embryonic mortality and bovine embryo viability assessment methods
Bibliographic record
Abstract
Animal reproductive biotechnology is continually evolving. Significant advances have been made in our understanding of early embryonic mortality and embryo development in domestic animals, which has improved the selection and success of in vitro technologies. Yet our knowledge is still relatively limited such that identifying a single embryo with the highest chance of survival and development for transfer remains challenging. While invasive methods such as embryo biopsy can provide useful information regarding the genetic status of the embryos, morphological assessment remains the most common evaluation. A recent shift, however, favors alternative, adjunct approaches for non-invasive assessment of an embryo's viability and developmental potential. Various analytical techniques have facilitated the evaluation of cellular health through the metabolome, the assessment of end products of cellular metabolism, or by analyzing spent media for small RNAs. This review discusses the application of noninvasive approaches for ascertaining the health and viability of in vitro-produced bovine embryos. A comparative analysis of noninvasive techniques for embryo assessment currently being investigated in cattle and humans is also discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".