Delineation of Eastern Beaufort Sea Sub-regions Using Self-Organizing Maps Applied to 17 Years of MODIS-Aqua Data
Bibliographic record
Abstract
Satellite observations are an integral component of long-term Arctic Ocean monitoring and help identifying changes resulting from climate warming. A Self-Organizing Maps (SOM) approach was applied to four-day composite satellite images of the Eastern Beaufort Sea (EBS) acquired by the MODerate resolution Imaging Spectroradiometer over the period 2003–2019. Using sea-surface temperature (SST), suspended particulate matter concentration (SPM) and chlorophyll-a concentration (Chl-a) as input the EBS was partitioned into six biogeochemical regions. The SOM approach revealed region-specific mean conditions and seasonal cycles for all properties, particularly for SPM and Chl-a. Three of the six regions, located on the continental shelf, had the highest SST, SPM and Chl-a with earlier maxima compared to the remaining three regions which comprised the shelf edge, Canada Basin and Amundsen Gulf. While mean and maximum SST did not exhibit significant trends over the 17 years of observations, the annual maximum SST in Amundsen Gulf was reached significantly earlier in recent years compared to the start of the time series. With the exception of Amundsen Gulf, sea-ice concentration (SIC) derived from microwave satellites declined throughout the study area; monthly trends showed dramatic SIC declines in regions on the shelf during May and June, and in Canada Basin during August. Correlation analysis of properties within and between regions showed that SST and SIC were driven by large scale processes while SPM and Chl-a showed regional features. SST and Chl-a in the regions nearest the Mackenzie River showed a strong relationship during seasonal warming. The SOM approach, applied to 17 years of satellite data, revealed spatially distinct marine units with unique characteristics, emphasizing the need for regional considerations when assessing the impact of climate warming in the Arctic Ocean.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".