Prediction of Spring Elbe Discharge Based on Stable Teleconnections with Winter Global Temperature and Precipitation
Bibliographic record
Abstract
Abstract The predictability of Elbe streamflow anomalies during spring is examined using previous winter sea surface temperature (SST), temperature over land (TT), and precipitation (PP) anomalies. Based on running correlation analysis, the authors identify several regions where the spring streamflow anomalies are stable correlated with SST, TT, and PP anomalies from the previous winter. During the period 1902–71 the Elbe spring streamflow is stable correlated with previous winter PP anomalies from its catchment area, with TT anomalies from the Black Sea–Caspian Sea region, northwestern Europe, and northern Canada as well as with SST anomalies from the tropical Pacific, the Indian Ocean, and several regions of the North Pacific and the North Atlantic. An index based on winter SST, TT, and PP anomalies from these regions is highly significantly correlated with spring streamflow anomalies during this period. Based on SST, TT, and PP anomalies from stable correlated regions, a forecast scheme is developed and applied to predict spring streamflow anomalies during the last decades. The prediction based on this statistical scheme represents a marked improvement relative to the forecast based on teleconnection indices that are traditionally used for streamflow prediction.
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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 | 0.000 | 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 | 0.000 | 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 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".