Comparison of Neural Networks to Ormsbee's Method for Rain Generation - applied to Toronto, Ontario
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
Rainfall rate is a key input function for the analysis and design ofhydrologic and hydraulic systems. One common problem with existing records of rain is that the time increments are not fine enough for use in mban storm water models. To solve this problem, observed rainfall data can be disaggregated into shorter time steps. In this chapter two artificial neural networks are used to disaggregate hourly rainfall data into 5 min time steps. One model is a multi-layer perceptron (MLP) with a fast back propagation learning algorithm, while the other is a radial basis function (RBF) network with an orthogonal least-squared error-learning algorithm. Both models are described and evaluated. It is shown that the RBF model performed poorly and its use is not recommended for rainfall disaggregation. However the MLP model achieved generally comparable results to Ormsbee's continuous detenninistic model, and did better in the prediction of maximum incremental rainfall depth, but at significantly higher computational effort.
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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) | 0.002 | 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 it