Bovine Oocyte Transcriptome in Relation to Chromatin Configuration of the Germinal Vesicle
Bibliographic record
Abstract
Welcome to all, I am very pleased to welcome you to the 4 th Mammalian Embryo Genomics meeting hosted by the EmbryoGENE Network and Université Laval.The first edition of this international conference was held in Quebec City in 2002 and aimed at bringing together internationally recognised scientists in the genomics field, in an attempt to coordinate activities in the area of embryo genomics and create collaborations in this field of research.A little more than a decade and two other Mammalian Embryo Genomics meetings later (Paris, France and Bonn, Germany), we can claim: mission accomplished!Although researchers still face many challenges, the number of international collaborations is ever increasing and many groups, consortiums and networks were born from discussion held at these meetings.These collaborations have no doubt contributed to the exciting technological advances and findings, which will be discussed this week.A special focus will be laid to large-scale genomics and epigenomics analyses, especially in light of the conclusion of the EmbryoGENE Network and its extensive database of transcriptomics and epigenetics data.Recent progress and questions in fields such as oocyte competence, early embryo development, maternal environment, assisted reproductive technologies, transcriptomics and epigenetics will also be addressed in the mammalian and domestic animal contexts.We look forward to an exciting and interactive meeting.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".