Modeling of hourly NO<sub><i>x</i></sub> concentrations using artificial neural networks
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
Modeling of ambient air quality is an important component of urban air quality management. Artificial neural network (ANN) modeling may offer advantages in understanding processes that follow nonlinear, complex relationships. Artificial neural network modeling is a black-box method where relationships describing complex situations are not necessarily known. The ANN models learn patterns based on historical data, and then conduct simulations based upon these patterns. The objective of this study was to evaluate the feasibility of using an ANN to predict ambient hourly concentrations of oxides of nitrogen (NO x ) in an industrial corridor adjacent to Edmonton, Alberta. A standard 4-layer back-propagation network was used to predict ambient hourly NO x concentrations using industry stack emission rates, meteorological data, and traffic counts as input variables. The resulting model fit (R 2 of 0.63) and precision of model prediction (root mean square error of 1.8 × 10 3 ppm as NO 2 ) suggested that ANN modeling shows promise for predicting NO x behaviour; however, further work is necessary to improve its forecasting ability.Key words: urban air quality, airshed, modeling, artificial neural network.
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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 | 0.001 | 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) | 0.000 | 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