Simulating dispersal in a complex coastal environment: the Eastern Shore Islands archipelago
Bibliographic record
Abstract
Abstract The Eastern Shore Islands (ESI) archipelago on the Scotian Shelf supports a rich variety of biogenic habitats and associated diversity of coastal species. The unique and complex geometry of the ESI coastline has a significant impact on circulation and, correspondingly, influences the dispersal of nearshore organisms. For many coastal areas, the ability to accurately resolve the dispersal processes is contingent on the availability of oceanographic models that can resolve fine-scale coastal boundary conditions, including coastlines and bathymetric features. We applied a high-resolution ocean circulation model and Lagrangian particle tracking in the ESI to simulate dispersal of nearshore organisms. Our results revealed predominant southwest–northeast transport that was associated with a nearshore reversal flow. While transport among different zones of the study region is mainly determined by residual currents over the long term, tidal currents dominate patterns of particle dispersal over shorter time scales. An analysis of Lagrangian coherent structures found that they were consistently associated with the mouths of bays, demonstrating that the islands and associated oceanographic processes promote self-retention. These results highlight how complex coastlines and associated oceanographic processes promote retention and underline the need to resolve these fine-scale physical and oceanographic features when estimating biophysical dispersal in the coastal environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".