Effects of temperature and body size on covering in green sea urchin, Strongylocentrotus droebachiensis
Bibliographic record
Abstract
Green sea urchin, Strongylocentrotus droebachiensis, is common in shallow subtidal rocky reef habitats in the northwestern North Atlantic. It is an important ecosystem engineer, capable of overgrazing on kelp beds to form urchin barrens. Green sea urchin often exhibits a ‘covering’ or ‘hatting’ response, whereby it adorns its test with various materials available in the habitat. Covering is presumably a response to an environmental cue, however, definitive reasons for covering have not yet been described in the literature. We carried out a 2-week laboratory experiment to test the predictions that green sea urchin covers (1) less in cold (2°C) and warm (14°C) seawater, as it is outside of thermal optima; (2) more with live rhodolith fragments than with blue mussel shell fragments or denatured rhodolith fragments; and (3) more when small (1 to 2 cm in test diameter, t.d.) than large (4 to 5 cm t.d.) in still water conditions. Sea urchins were acclimated and exposed to one of three temperatures (2, 8, or 14°C) in containers within water baths. Each container, containing one sea urchin, was given a covering material type (live rhodoliths, denatured rhodoliths, or blue mussel shells), whereby the resultant degree of covering exhibited by sea urchins was assessed. Model outputs supported the predictions that temperature and sea urchin size affect covering in green sea urchin, while rejecting the prediction that covering material type affects the degree of covering in green sea urchin. Our results help establish a baseline for temperature-induced covering thresholds in green sea urchin under still water conditions, values which are not currently covered in the literature.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".