A novel cross‐shore transport mechanism revealed by subsurface, robotic larval mimics: Internal wave deformation of the background velocity field
Bibliographic record
Abstract
Abstract Coastal physical processes are essential for the cross‐shore transport of meroplanktonic larvae to their benthic adult habitats. To investigate these processes, we released a swarm of novel, trackable, subsurface vehicles, the Mini‐Autonomous Underwater Explorers (M‐AUEs), which we programmed to mimic larval depth‐keeping behavior. The M‐AUE swarm measured a sudden net onshore transport of 30–70 m over 15–20 min, which we investigated in detail. Here, we describe a novel transport mechanism of depth‐keeping plankton revealed by these observations. In situ measurements and models showed that, as a weakly nonlinear internal wave propagated through the swarm, it deformed surface‐intensified, along‐isopycnal background velocities downward, accelerating depth‐keeping organisms onshore. These higher velocities increased both the depth‐keepers' residence time in the wave and total cross‐shore displacement, leading to wave‐induced transports twice those of fully Lagrangian organisms and four times those associated with the unperturbed background currents. Our analyses also show that integrating velocity time series from virtual larvae or mimics moving with the flow yields both larger and more accurate transport estimates than integrating velocity time series obtained at a point (Eulerian). The increased cross‐shore transport of organisms capable of vertical swimming in this wave/background‐current system is mathematically analogous to the increase in onshore transport associated with horizontal swimming in highly nonlinear internal waves. However, the mechanism described here requires much weaker swimming speeds (mm s−1 vs. cm s−1) to achieve significant onshore transports, and meroplanktonic larvae only need to orient themselves vertically, not horizontally.
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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 | 0.000 | 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".