Seasonal prediction of North Atlantic sea surface temperature anomalies using the LSTM machine learning method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Sea surface temperature anomalies (SSTAs) over the North Atlantic (NA) have a significant impact on the weather and climate in both local and remote regions. This study first evaluated the seasonal prediction skill of NA SSTA using the North American multi-model ensemble and found that its performance is limited across various regions and seasons. Therefore, this study constructs models based on the long short-term memory (LSTM) network machine learning method to improve the seasonal prediction of NA SSTA. Results show that the seasonal prediction skill can be significantly improved by LSTM models since they show higher capability to capture nonlinear processes such as the impact of El Niño-Southern Oscillation on NA SSTA. This study shows the great potential of the LSTM model on the seasonal prediction of NA SSTA and provides new clues to improve the seasonal predictions of SSTA in other regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it