Modelling nitrogen composition in streams on the Boreal Plain using genetic adaptive general regression 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
Increased release of nitrogen to hydrological networks due to watershed disturbance may cause aquatic problems and affect water uses. Therefore, effective nitrogen modelling is an important element of total watershed management. The objective of this study was to develop an artificial neural network modelling tool to predict nitrogen concentrations in streams using easily accessible data as model inputs. Genetic adaptive general regression neural network (GA-GRNN) models were applied to predict nitrate, ammonium, and total dissolved nitrogen concentrations in three forested watersheds in Alberta, Canada. The performance and generality of the developed models for dry and wet weather conditions in the studied watersheds were verified by the coefficient of multiple determination, the root mean squared error, swapping the testing and validation data sets, and plotting measured and predicted values over time. The successful application of GA-GRNN models to predict nitrogen compositions in the watersheds by using five major input variables and relevant time-lagged inputs, fully demonstrated the models’ generality. It implies the high potential of applying GA-GRNN models for predicting other surface water quality parameters on other watersheds with similar or different characteristics.
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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) | 0.000 | 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