Comparison of SMOS, SMAP and <i>In Situ</i> Sea Surface Salinity in the Gulf of St. Lawrence
Bibliographic record
Abstract
The Gulf of St. Lawrence (GSL) is an Eastern Canada semi-enclosed sea under the influence of the freshwater discharge from the Great Lakes – St. Lawrence River drainage basin. Studying the variability of oceanographic conditions in the GSL under a changing climate is important for ecosystem and fisheries management. To supplement the available in situ sea surface salinity (SSS) measurements with satellite SSS data, this study compares all available years of Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) satellite SSS to in situ SSS observations. Despite the relatively cold water and proximity to land and sea ice, the satellite SSS is able to capture the interannual variability and annual cycle of SSS in the GSL, with correlations ranging from 0.80 to 0.85 in the Southern GSL, and 0.22 to 0.77 in the Northern GSL. All satellite SSS data products were able to detect the very low salinity year of 2017 in the Southern GSL.
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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 | 0.001 | 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".