Quantile regression of satellite-derived CDOM for river plume dispersion in southern Hudson Bay
Bibliographic record
Abstract
Physical and biogeochemical processes in coastal waters are shaped by salinity variation induced by river water mixing. As salinity is intrinsic to any aquatic ecosystem, any change will challenge the ecological framework. Thus, space-based monitoring of salinity in regions susceptible to salinity changes, such as estuaries and nearshore waters, is the need of the moment and supports Sustainable Development Goal 14 of the United Nations. Therefore, the salinity monitoring process addresses salinization/de-salinization issues of transitional waters. Current sea surface salinity products from satellites, such as Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), exclude shallow waters, creating a salinity data gap in the land-ocean continuum. Such a gap in salinity considerably prevents continuous and synoptic river plume monitoring using satellite observations. Light absorption properties were studied in the coastal waters of Hudson Bay and James Bay, the shallow inland seas of Canada, to overcome the problem. River water carries terrestrial signals into the estuarine and coastal seas through dissolved organic matter (DOM) and inorganic sediments. DOM and sediments in seawater interact with the visible spectrum of solar radiation that can be mapped using ocean color remote sensing. Colored dissolved organic matter (CDOM), the optically active portion of DOM, dominates the light absorption budget at 412 nm in the coastal waters of Hudson Bay and James Bay, followed by the suspended inorganic solids. Hudson Bay waters were clearer relative to James Bay, with a higher content of river-sourced CDOM. The concentration of these river-derived optical tracers decayed offshore. CDOM underwent conservative dilution with increasing salinity, while suspended sediments were non-conservative. Therefore, based on the conservative CDOM and salinity relationship, a quantile regression approach was developed to quantify the Nelson River water dispersion in Hudson Bay using CDOM concentrations retrieved from moderate resolution imaging spectroradiometer (MODIS) images. This novel method permits the mapping of surface river water mixing with sea waters in terms of the distance from the river mouth corresponding to different percentages of diluted river water and the direction of river water transport. Such a strategy assists in coastal management, such as identifying the marine conservation area's geographic boundaries and conducting water quality tests to assess the health of coastal waters.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".