The variability of the sediment plume and ocean circulation features of the Nass River Estuary, British Columbia
Bibliographic record
Abstract
The Nass River discharges into Nass Bay and Iceberg Bay, which are adjoining tidal inlets located within the northern inland waters of British Columbia, Canada. After the Skeena River, the Nass River is the second longest river within northern British Columbia, which discharges directly into Canadian waters of the Pacific Ocean. It is also supports one of the most productive salmon fisheries in northern British Columbia. The Nass River discharges into the eastern end of Nass Bay. Nass Bay, in turn feeds into Portland Canal and the fresh surface waters then flows westward to the Pacific Ocean via Dixon Entrance. The tides in Northern British Columbia are very large with a tidal height range of just over 7 m. Nass Bay is a shallow inlet of less than 10 km in length with typical water depths of than 10 m or less. The existing knowledge of oceanographic processes in Nass and Iceberg Bays was rudimentary until three years ago, when the first modern oceanographic measurements were obtained. In this study, the seasonal and tidal variability of the lateral extent of the Nass River surface plume is mapped from analyses of Landsat satellite data spanning the period from 2008 to 2015. A high resolution coupled three dimensional (3D) hydrodynamic model was developed and implemented, within the widely used and accepted Delft3D modeling framework, which was forced and validated using recent 2013–2016 in-situ oceanographic measurements. The combined satellite and numerical modeling methods are used to study the physical oceanographic and sediment transport regime of Nass and Iceberg Bays and the adjoining waters of Portland Inlet and Observatory Inlet. The ocean circulation of Nass and Iceberg Bays was found to be dominated by tidal currents, and by the highly seasonal and variable Nass River freshwater discharges. Complex lateral spatial patterns in the tidal currents occur due to the opening of the southwestern side of Nass Bay onto the deeper adjoining waters of Iceberg Bay. Surface winds are limited to a secondary role in the circulation variability. The sediment dynamics of the Nass Bay system features a very prominent surface sediment plume present from the time of freshet in mid-spring through to large rainfall runoff events in the fall. The time-varying turbidity distribution and transport paths of the Nass River sediment discharges in the study area were characterized using the model results combined with an analysis of several high-resolution multi-year Landsat satellite data sets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".