Kinetic Vitrification of Spermatozoa of Vertebrates: What Can We Learn from Nature?
Bibliographic record
Abstract
Why sperm? Cryobiology had actually started from freezing sperm. We will skip all those very early anecdotes but should mention the Spallanzani attempt to freeze frog semen in the 18th century [Spallanzani, 1780]. Cryobiology as a science started with revolutionizing work of Father Luyet and other scientists of the late 1930’s and 1940’s, who we can collectively call “the pioneers of the cryobiological frontiers” (see the following sub-Chapter). There were several reasons why sperm was chosen, which included easiness in obtaining the samples, clear evidence of viability (moving – not moving, though later it was figured that everything was not so easy in this sophisticated living “cruise missile”), and importance for the farming industry with the emergence of systematic selective breeding (especially in cattle) with a powerful tool – artificial insemination (AI). AI started with the revolutionary work of W. Heape, I.I. Ivanov and other scientists at the dawn of the 20th century and was further developed by V.K. Milovanov in the 1930’s as a viable breeding technology (see [Foote,
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".