The variation of floods in the middle reaches of the Yangtze River and its teleconnection with El Niño events
Bibliographic record
Abstract
Abstract. Middle reaches of the Yangtze River are the worst flooded segments along the Yangtze River. It is important to understand and study the variations of frequency and magnitude of historical floods in this area and how were they related to or affected by EI Niño in a long historical period. We applied the statistics and time series to study and analyze historical floods (1470-2000) and EI Niño events (1525-1995). The results show that the more floods occurred in the latest 200 years. The power-spectral analysis suggests the main cycle of flood variation is longer than that of EI Niño events. El Nino shows the fluctuations of about 2-year and 3~4 year period while the flood variation is not so significant but can also be identified in the period of 2, 8 and 40 years (it exceeds the level of confidence 0.03). Time series analyses of the fluctuation of flood and El Nino indicate that there is a significant correlation between the two at both high and moderate frequency sections. The result shows that the response of the floods along the middle reaches of the Yangtze River to the effects of El Nino events is not only delayed one or more than one year as suggested by many Chinese scientists, but it also can be somewhat longer delayed up to about 8 years. The result also indicates that the shorter the interval of EI Niño events, the sooner the flood events follows. In other words, flood could be delayed with longer time if the interval time of EI Niño events is longer.
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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 | 0.001 |
| 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) | 0.001 | 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 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".