Quantitative Analysis of Locomotive Behavior of Human Sperm Head and Tail
Bibliographic record
Abstract
Sperm selection plays a significant role in in vitro fertilization (IVF). Approaches for assessing sperm quality include noninvasive techniques based on sperm morphology and motility as well as invasive techniques for checking DNA integrity. In 2006, a new device using hyaluronic acid (HA)-coated dish for sperm selection was cleared by the Food and Drug Administration (FDA) and entered IVF clinics. In this technique, only sperms with DNA integrity bind to the HA droplet, after which these bound sperm stop revealing head motion and their tail movement becomes more vigorous. However, selecting a single sperm cell from among HA-bound sperms is ad hoc in IVF clinics. Different from existing sperm tracking algorithms that are largely limited to tracking sperm head only and are only able to track one sperm at a time, this paper presents a multisperm tracking algorithm that tracks both sperm heads and low-contrast sperm tails. The tracking results confirm a significant correlation between sperm head velocity and tail beating amplitude, demonstrate that sperms bound to HA generally have a higher velocity (before binding) than those sperms that are not able to bind to HA microdots, and quantitatively reveal that HA-bound sperms' tail beating amplitudes are different among HA-bound sperms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".