Larval cross‐shore transport estimated from internal waves with a background flow: The effects of larval vertical position and depth regulation
Bibliographic record
Abstract
Abstract Cross‐shore velocities in the coastal ocean typically vary with depth. The direction and magnitude of transport experienced by meroplanktonic larvae will therefore be influenced by their vertical position. To quantify how swimming behavior and vertical position in internal waves influence larval cross‐shore transport in the shallow (~ 20 m), stratified coastal waters off Southern California, we deployed swarms of novel, subsurface larval mimics, the Mini‐Autonomous Underwater Explorers (M‐AUEs). The M‐AUEs were programmed to maintain a specified depth, and were deployed near a mooring. Transport of the M‐AUEs was predominantly onshore, with average velocities up to 14 cm s−1. To put the M‐AUE deployments into a broader context, we simulated > 500 individual high‐frequency internal waves observed at the mooring over a 14‐d deployment; in each internal wave, we released both depth‐keeping and passive virtual larvae every meter in the vertical. After the waves' passage, depth‐keeping virtual larvae were usually found closer to shore than passive larvae released at the same depth. Near the top of the water column (3–5‐m depth), ~ 20% of internal waves enhanced onshore transport of depth‐keeping virtual larvae by ≥ 50 m, whereas only 1% of waves gave similar enhancements to passive larvae. Our observations and simulations showed that depth‐keeping behavior in high‐frequency internal waves resulted in enhanced onshore transport at the top of the water column, and reduced offshore dispersal at the bottom, compared to being passive. Thus, even weak depth‐keeping may allow larvae to reach nearshore adult habitats more reliably than drifting passively.
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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) | 0.000 | 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".