Plume dispersion from the Nelson and Hayes rivers into Hudson Bay using satellite remote sensing of CDOM and suspended sediment
Bibliographic record
Abstract
Change in the dispersion pattern of Arctic river plumes due to climate change and hydroelectric regulation is challenging to monitor, calling for synoptic and continuous observation using satellite remote sensing. Algorithms for colored dissolved organic matter (CDOM) and total suspended solids (TSS) were applied to moderate resolution imaging spectroradiometer (MODIS) imagery to study Nelson and Hayes river plume dispersion into southwestern Hudson Bay, employing quantile regressions to capture dispersion variability along a freshwater–marine gradient. MODIS-derived CDOM and TSS quantile concentrations (Q0.05–Q0.95) decreased exponentially with distance from the Nelson River mouth. The Q0.95 asymptote marked the offshore extent of the river plume and was used to determine the marine and river water fractions of surface water in southwestern Hudson Bay. At about 125 km from the Nelson River mouth, CDOM was reduced by 75% of its river mouth values. Owing to the significant co-variability between CDOM dilution and river discharge, a 0.25 river water fraction was estimated at this distance, which varied by ±35 km during flood and ebb flows. Anti-cyclonic winds transported the river plume along the 54° azimuth towards central Hudson Bay, while cyclonic winds propagated the plume eastward along the south shore. Particle settling in the coastal waters and resuspension events from mudflats and/or bank erosion caused non-significant relationships between TSS and river discharge. This non-conservative behavior renders TSS a less useful optical tracer of Nelson and Hayes river water in southwestern Hudson Bay. The novel quantile regression approach for defining boundaries of river water dilution in transitional waters may provide helpful information for coastal management on a spatial scale of tens to hundreds of kilometers, ranging from near real-time monitoring to seasonal and multi-year studies.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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 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".