Hydrological Drought Instantaneous Propagation Speed Based on the Variable Motion Relationship of Speed‐Time Process
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 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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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