A comparison of artificial neural networks and multiple regression methods for the analysis of pilot-scale data
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
Pilot-scale testing is widely used in the drinking water supply industry to test treatment theories, develop new processes, and enhance process operations. The data sets derived from pilot testing are usually small owing to financial and time considerations. The analysis of such data is extremely complex, since treatment processes are highly complex and nonlinear. Multiple regression analysis is widely considered to be the best available technology for analysing data collected from pilot-scale experiments in the drinking water supply industry. Unfortunately, this technique is limited in its ability to handle the combinations of fixed and random variables that are characteristic of water treatment processes. This paper demonstrates the applicability and advantages of artificial network modelling for pilot-scale data analysis. Data collected at two separate pilot-scale facilities are analysed using the artificial neural network (ANN) technique and multiple regression methods, and performance assessments of the two are made. Key words: artificial neural networks, pilot plant, data analysis.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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