Lagrangian observations of estuarine residence times, dispersion, and trapping in the Salish Sea
Bibliographic record
Abstract
Estuaries are important mediators in the transport of freshwater, sediment, and dissolved material into the ocean, and many studies have investigated mean estuarine circulation. However, it is not clear how descriptions of the mean circulation are linked to the motion of individual water parcels, or of discrete items floating within the system, nor how they related to the variability in this dispersal. An obvious approach to characterizing this variability is to actually measure dispersal by tracking Lagrangian drifters. Recent technological advances have dramatically decreased the cost of satellite-tracked and monitored drifters, making longer deployments of large numbers of expendable drifters much more feasible. Here we take advantage of this opportunity to directly characterize the seaward flow and dispersion of surface water in a relatively well-studied estuarine system (the Salish Sea on the NE Pacific coast), combining 2200 drifter-days of data from more than 400 tracked drifters with the deployment of nearly 6000 traditional driftcards. Residence times in different parts of the system estimated from the drifter observations are similar but systematically lower than those derived from other methods, and we speculate that this may be because surface drifts are faster than layer-mean drifts in a fjord-type estuarine system. We also find that drifters tend to ground on shore, and that time to-grounding from different source locations has an approximately exponential distribution with a mean of only a few days, much less than the transport time to the ocean. The estuary is therefore a highly efficient trap for floating objects. Finally, we quantify a dispersion coefficient, finding that dispersion is a critical component of transport in parts of the Salish Sea, often much more important than mean advection. The mean is visible only after averaging for weeks or months.
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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 | 0.001 | 0.001 |
| 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".