Bibliographic record
Abstract
Flood forecasting necessarily relies on a number of technologies, namely, precipitation measurement at high resolution for large areas; numerical modelling of the hydraulics and hydrology of runoff and routing; and innovative information technology for synthesizing critical information needed for emergency response and risk management.Data sources for precipitation inputs can come from weather radar and automated rain gauges.Regional and urban hydrologic modelling is increasingly based on distributed physics-based hydrologic models that can leverage the high definition rainfall derived from radar and rain gauge measurements.Statistical quality control in real-time is necessary to transform radar-based precipitation estimates into accurate hydrologic model input, which serves as an input to a physics-based distributed runoff model.Geospatial data are necessary for setup and parameterization of such distributed models.Real-time precipitation estimates from radar and rain gauge monitoring are coupled with the gridded representation of the watershed to produce warning notifications at distributed locations throughout urban, peri-urban, and rural/natural watersheds.Quantification of uncertainties and sources of error are examined within the pursuit of successful flood forecasting.The following case studies illustrate distributed hydrologic forecasting across a range of space-time scales.This paper presents forecasting performance, an evaluation of accuracy, and discussion of factors that contribute to uncertainty in flood forecasts and risk reduction.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".