Cnida sequestration in aeolid nudibranchs: variability and retention time of sequestered cnidae in the opalescent sea slug, <i>Hermissenda crassicornis</i> (Gastropoda, Nudibranchia)
Bibliographic record
Abstract
Aeolid sea slugs can isolate and store stinging cnidae obtained from their cnidarian prey, presumably for their own defence. There are 30 different varieties of cnidae, identified by their unique structure. The aeolids engulf the cnidae and store them in a functional state at the tips of their cerata. Although the process of cnida sequestration is reasonably well understood in aeolids, two critical questions remain: (1) are cnida types uniformly distributed among the cerata and (2) how long do sequestered cnidae persist? I collected opalescent sea slugs (Hermissenda crassicornis (Eschscholtz, 1831)) from Barkley Sound, British Columbia, Canada, and determined the cnida complements in four cerata per individual by microscope. The cnida complements differed between cerata from different body regions within the individual (values of Whittaker’s dissimilarity index from 2.5% to 36.3%). Furthermore, the cnidae varieties in low abundances are not consistently present within an individual. I also found that H. crassicornis fed a non-cnidarian diet lost cnidae over time, but the cnidarian-fed individuals did not: 3 of 10 H. crassicornis on the non-cnidarian diet lacked cnidae completely at 42 days. Future studies of cnida sequestration should be mindful that one ceras does not give an adequate representation of the distribution of sequestered cnidae.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".