Evaluation of satellite-based sea surface salinity derived from two distinct spectral domains over the coastal waters of the St. Lawrence Estuary and Gulf
Bibliographic record
Abstract
Brightness temperature is operationally used to retrieve sea surface salinity (TB-SSS) over the global ocean, but is contaminated by land and sea ice in close proximity. Ocean color can be used to retrieve SSS (OC-SSS) via the relation between color and salinity, but this relation is only valid over the coastal ocean with terrestrial influence. Important ecological areas exist where both spectral domains can provide SSS estimates. Here we compare these estimates over the St. Lawrence Estuary and Gulf in Eastern Canada, where a large collection of near-surface in situ salinity measurements is available. While TB-SSS faces a significant limitation in undersampling spatial variability, OC-SSS is predominantly hindered by cloud cover. Offshore, TB-SSS data are considerably more abundant than OC-SSS data, the latter of which are available only about 30% as often as the former. However, OC-SSS estimates extend into more nearshore areas, such as the St. Lawrence Estuary. Additionally, OC-SSS estimates are more accurate, with a root mean square difference of 0.46 g kg −1 compared to 0.79 g kg −1 for TB-SSS. We employed each of these satellite-derived SSS products to compare the pronounced freshwater pulse of 2017 and post-tropical storm Dorian of fall 2019, finding that short-lived events were better captured by the OC-SSS product. In contrast, the TB-SSS product offered more extensive temporal coverage but smoothed out such events. Our analyses underscore the need for higher-resolution satellite salinity-sensors in coastal studies. In the meantime, ocean color data resolves submesoscale features and can help enhance our understanding of these dynamic environments.
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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 | 0.001 |
| 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 | 0.001 | 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".