Physical tidepool characteristics affect age- and size-class distributions and site fidelity in tidepool sculpin (<i>Oligocottus maculosus</i>)
Bibliographic record
Abstract
Rapidly changing environments pose unique challenges to the resident organisms. Tidepools in coastal environments vary in biophysical characteristics spatially and temporally, and how they vary determines their short- and long-term suitability as habitats and therefore influence on the distributions of tidepool organisms. Biophysical effects on distribution could differ between age classes, depending on their intrinsic ontogenetic requirements and dominance relations. In this study, we investigate the influence of physical pool characteristics on the site fidelity and population distribution of tidepool sculpin (Oligocottus maculosus Girard, 1856). We assessed short-term recapture of marked individuals and size-class distribution among four pool sets. The proportion of adults varied between pools primarily in association with water temperature and pool volume. Smaller adult and larger juvenile fish occupied warmer, small-volume pools, whereas larger adults occupied larger, cooler pools. Between 24% and 56% of marked fish were recaptured, with a higher probability of recapture in pools with “smooth” basins than in those with more rugose basins. Few fish moved among study pools, but the proportion of adults declined with repeated sampling, suggesting greater pool fidelity of juveniles. These results illustrate that intrinsic habitat features influence age- and size-class distributions in a resident tidepool sculpin species, with corresponding consequences for site fidelity.
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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 | 0.000 | 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) | 0.001 | 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".