Ovarian fluid impacts flagellar beating and biomechanical metrics of sperm between alternative reproductive tactics
Bibliographic record
Abstract
Alternative reproductive tactics (ARTs) are prevalent in nature, where smaller parasitic males typically have better sperm quality than larger territorial guard males. At present, it is unclear what is causing this phenomenon. Our objective was to gain insights into sperm form and function by examining flagellar beating patterns (beat frequency, wave amplitude, bend length, bend angle, wave velocity) and biomechanical sperm metrics (velocity, hydrodynamic power output, propulsive efficiency) of wild spawning Chinook salmon ARTs. Ovarian fluid and milt were collected to form a series of eight experimental blocks, each composed of ovarian fluid from a unique female and sperm from a unique pair of parasitic jack and guard hooknose males. Sperm from each ART were activated in river water and ovarian fluid. Flagellar parameters were evaluated from recordings using high-speed video microscopy and biomechanical metrics were quantified. We show that ART has an impact on flagellar beating, where jacks had a higher bend length and bend angle than hooknoses. Activation media also impacted the pattern of flagellar parameters, such that beat frequency, wave velocity and bend angle declined, while wave amplitude of flagella increased when ovarian fluid was incorporated into activation media. Furthermore, we found that sperm from jacks swam faster than those from hooknoses and required less hydrodynamic power output to propel themselves in river water and ovarian fluid. Jack sperm were also more efficient at swimming than hooknose sperm, and propulsive efficiency increased when cells were activated in ovarian fluid. The results demonstrate that sperm biomechanics may be driving divergence in competitive reproductive success between ARTs.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".