Biophysical models reveal the role of tides, wind, and larval behaviour in early transport and retention of Atlantic herring (<i>Clupea harengus</i>) in the Celtic Sea
Bibliographic record
Abstract
Fish are adapted to spawn where their larvae will be retained in, or transported to, suitable juvenile habitat. Variability in circulation and behaviour produces variation in larval transport with consequences for recruitment. A biophysical model was used to simulate early larval dispersal of Celtic Sea herring (Clupea harengus) during wintertime in 6 years (2002, 2003, 2004, 2005, 2008, 2010). After 30 days, particles occurred in three areas: inshore Celtic Sea, offshore Celtic Sea, and southern Irish Sea, with the majority (70%–78% on average) of particles retained in the Celtic Sea. Inclusion of tidal forcing increased transport to the Irish Sea and decreased transport offshore, as did release during a spring tide. Retention in the Celtic Sea was increased by diel vertical migration and decreased by horizontal diffusion. Strong and frequent west to southwest winds increased transport offshore, while strong and frequent east to south-southeast winds increased inshore retention in the Celtic Sea and transport into the Irish Sea. The study shows how tides, winds, and behaviour influence larval transport and retention and highlights potential impacts of climate change on population persistence.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".