How useful are progressive vector diagrams for studying coastal ocean transport?
Bibliographic record
Abstract
Progressive vector diagrams (PVDs) have been used to estimate transport in the coastal ocean from point measurements of velocity time series, although they strictly only approximate true particle trajectories in regions where the currents are spatially uniform. Currents in most coastal regions vary significantly in both space and time, making coastal transport estimates from PVDs questionable. Here, we used synoptic surface currents measured over periods of several months by HF radars in two coastal areas with distinctly different circulation features (the Gulf of Eilat, a deep, semi‐enclosed basin, and the Delaware Bay mouth, a coastal estuarine outflow) to assess the time scales over which PVD paths computed from velocity time series at fixed points separate from particle trajectories computed from two‐dimensional measured horizontal currents that vary in both space and time. In both study regions, PVDs and particle trajectories separate by 1 km over a mean time period of 7–10 h with no significant month‐to‐month variation in either region. The separation time statistics presented here should serve as a strong caution for investigators motivated to estimate transport using only point measurements.
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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 | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".