Spawning events in small and large populations of the green sea urchin <i>Strongylocentrotus droebachiensis</i> as recorded using fertilization assays
Bibliographic record
Abstract
During the winter and spring of 2002 and 2003, we used time-integrated fertilization assays to monitor sperm availability in three populations of the green sea urchin Strongylocentrotus droebachiensis in Maine: a naturally occurring population of >40,000 urchins and two smaller groups (<1,000) of transplanted urchins isolated from other aggregations. Episodes of sperm release coincided in two populations 10 km apart, suggesting that urchins were responding to a widespread environmental signal. We observed significant lunar periodicity in sperm release events for both of these populations. However, extensive spawning as shown by fertilization rates near 100% and a dramatic drop in gonad mass only occurred in the large natural population around the onset of thermal stratification and the spring phytoplankton bloom. By contrast, in the two small populations we observed low fertilization rates and little or no change in gonad mass. We speculate that a subset of males in these populations responded to a common external spawning signal, but that mass spawning is more likely to occur in large, dense populations where sperm concentrations reach high enough levels to trigger spawning in less responsive urchins.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".