Barnacle recruitment on ice-scoured shores in eastern Canada
Bibliographic record
Abstract
Previous observations in the St Lawrence Estuary (eastern Canada) suggested that larvae of intertidal barnacles (Semibalanus balanoides) would settle almost exclusively inside crevices on shores that are scoured by sea ice every winter. It was suggested that the strong ice scour in winter on that coast (which removes organisms outside of crevices) would select for such a larval behaviour. We tested the generality of this pattern by sampling other ice-scoured shores within the Gulf of St Lawrence system. In particular, we surveyed a shore in Nova Scotia where exposed habitats (subjected to strong ice scour in winter) are interspersed with sheltered habitats (which suffer milder ice scour). Such a topographical complexity might allow for the coastal larval pool to contain a proportion of larvae that have no particular settlement preference for crevices, as selective pressures for such a behaviour would be minimal in ice-sheltered habitats. Consistently with this notion, barnacle recruits (which appear after the winter ice melts) occurred abundantly both inside and outside of crevices across the shore in the spring seasons of 2005 and 2006. Average recruit density on rocky surfaces ranged between 337 and 588 recruits dm−2, depending on the habitat. It is therefore possible that barnacle recruitment patterns on ice-scoured shores may be affected indirectly by the structural complexity of the coast.
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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 | 0.001 |
| 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".