Cuticle and muscle variation underlying phenotypic plasticity in barnacle feeding leg and penis form
Bibliographic record
Abstract
Abstract Many aspects of barnacle body form are known to be developmentally plastic. Perhaps the most striking examples of such plasticity occur in their feeding legs and unusually long penises, the sizes and shapes of which can change dramatically and adaptively with changes in conspecific density and local water flow conditions. However, whether variation in overall appendage form is mirrored by structural responses in cuticle and muscle is not known. In order to determine how structural variation underlies phenotypic plasticity in barnacle appendages, we examined barnacles occurring at low and high population densities from one wave‐protected and one wave‐exposed site. We used histological sectioning and fluorescence microscopy of feeding legs and penises to compare cuticle thickness, muscle thickness, and muscle organization, and artificial penis inflation to compare penis extensibility. We observed striking differences in cuticle thickness, muscle thickness, and muscle organization between sites that differed in water velocity, but we found no clear differences associated with variation in conspecific density. Penis extensibility also did not differ consistently between sites. These results are consistent with an adaptive explanation for much of the remarkable and complex variation in barnacle feeding leg and penis morphology among sites that differ in water velocity.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".