The impact of satellite retrievals in a global sea‐surface‐temperature analysis
Bibliographic record
Abstract
Abstract An analysis of sea surface temperature (SST) is described. It incorporates in situ observations and retrievals from one microwave and three infrared sensors. Statistical interpolation is used to update the analysis daily on a global grid with a resolution of 1/3°. The background or first‐guess field is essentially the analysis from the previous day. Satellite retrievals and buoy observations undergo a thinning and all data is subjected to a careful quality control. A scheme to remove large‐scale biases from the satellite data is included, and its impact is assessed. Analysis error is estimated using two sources of independent data with similar results. The global average r.m.s. error is less than 0.4 K. Zonally averaged errors were computed over 15°‐wide latitude bands giving errors in the range 0.25 K to 0.5 K. The contributions from infrared and microwave data are found to be roughly of equal importance. The two data types are shown to be complementary, producing a significant improvement in analysis error when used together compared with the error obtained when only one is used. The analysis also compares favourably with SST analyses produced by three other centres. Copyright © 2008 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".