A Synoptic-Scale Comparison of Satellite Yukon River Mouth Temperature to In-Situ and Reanalysis Data During 2003–2020
Bibliographic record
Abstract
This paper combines remote sensing Sea Surface Temperature (SST), modeled salinity incorporating remote sensing data, reanalysis products, and in-situ data to study surface changes in previously difficult-to-access coastal Gulf of Alaska over the period 2003–2020. Despite river mouth SST warming from the beginning to the end of the study, data illustrate cooling of the Yukon from headwaters to the river mouth from 2011 to 2020. This result suggests a complex forcing in addition to warming, as well as the need for future examination with inverse models capable of assessing physical budgets for parameters like SST, river discharge, and atmospheric forcing. Here, open source data is combined with techniques such as eigenvalue analysis and a toy problem that uses machine-learning techniques to point to parameters related to SST change in the Yukon River mouth.The featured remote-sensing dataset in this study, the Group for High Resolution Sea Surface Temperature Microwave and Infrared (GHRSST-MWIR) has data from the MODIS sensor on the Aqua and Terra platforms [3] and also includes microwave-derived SSTs from the Advanced Microwave Scanning Radiometer. This dataset provides a unique look at nearly 20 years’ worth of SST data in an important NASA Climate Variability and Change focus area (CVC) [4; 5].To begin to point to which parameters may contribute to change over the interannual SST variability in the Norton Sound, this remote sensing data is combined in a principal components analysis with other parameters of modeled salinity, reanalysis air temperature, water vapor, and precipitation from MERRA-2, and in-situ datasets of river discharge from the Yukon river headwaters (VonFinster dataset) and the Yukon river mouth at the Norton Sound (Arctic Great Rivers Observatory).
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".