An analysis of the Lake Surface Water Temperature evolution of the world’s largest lakes during the years 2003-2020 using MODIS data
Bibliographic record
Abstract
The Lake Surface Water Temperature (LSWT) evolution is analysed in ten of the largest lakes in the world: Caspian Sea, Superior, Victoria, Huron, Michigan, Tanganyika, Baikal, Great Slave Lake, Erie and Ontario. The time span selected is 2003-2020 and the satellite product, MODIS Level 3 SST Thermal IR 8 Day 4km V2019.0. Results show warming trends ranging from 0.012⁰C/yr. in the Victoria Lake to 0.083 ⁰C/yr in the Baikal Lake. Results have been validated with the product MOD11L2 LSWT estimations for the years 2003-2014 in the Laurentian Great Lakes, obtaining correlations between 0.962 and a 0.998. The validation has been enlarged by considering Sentinel 3 observations from the Issyk-Kul lake, with a 0.99 correlation. The validation shows that the MODIS SST product is capable of estimating the LSWT parameter with a high precision.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".