Hydrological Drought Instantaneous Propagation Speed Based on the Variable Motion Relationship of Speed‐Time Process
Bibliographic record
Abstract
Abstract It is difficult to predict and track the propagation of a hydrological drought because it is hard to determine its propagation speed. We propose a useful framework for calculating the hydrological drought instantaneous propagation speed which includes the instantaneous development speed ( IDS ) and instantaneous recovery speed ( IRS ). First, the run theory was applied to subdivide the propagation of individual hydrological drought events into the development and recovery stages and to determine the individual propagation times (drought development duration and drought recovery duration). Then the hydrological drought instantaneous propagation speed of each hydrological drought event, including the IDS and IRS , were determined based on the variable motion relationship of speed‐time process commonly applied in physics. Finally, the optimal theoretical values of the IDS and IRS were evaluated using a cross‐validation method. Three hydrometric stations, located at the upstream catchment with less human activities influence, were chosen from different countries (China, the United States, and Germany) to demonstrate the satisfactory performance of this proposed framework. The results indicate that the variable motion relationship of speed‐time process can provide an assessment of the overall hydrological drought propagation and perform well for identifying the propagation time in these study areas. The optimal theoretical values of IDS (or IRS ) obtained by the variable motion relationship can simulate the actual drought development duration (or drought recovery duration) of hydrological drought well. The sensitivity of IDS (or IRS ) of hydrological drought is correlated with climate, catchment characteristics, and human activities that should be explored to improve hydrological drought propagation prediction.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".