Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The vitrification technology is used in the embryo freezing process. To ensure intracellular water removal and avoid ice crystal formation, the cumulus cells surrounding zygotes need to be removed. As operators have varying skill and long-time fatigue, it is challenging to completely remove the cumulus cells surrounding zygotes and reduce zygote loss inside the micropipette. The objective of this work is to develop a robotic zygote denudation system for the vitrification procedure. The robotic system enables accurate segmentation of cumulus cells for estimating the mass of cumulus zygote complexes (CZCs) inside the micropipette via the Res-Unet neural network. A mathematical model was built to describe the dynamic motion of CZCs inside the micropipette, and a model-based optimal controller was developed to aspirate and deposit CZCs inside the micropipette. To denude zygote completely, the Res-Attention neural network was used to detect cumulus cells for predicting the quality of zygote denudation. In the mouse zygotes experiments, the yield rate is 98.2% ± 1.6% and the denudation efficacy is 96.9% ± 0.2% for the robotic system. Compared with manual denudation, the survival rate and development rate of embryos cultured from zygotes denuded by the robotic system were both higher in subsequent vitrification procedure.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it