Modelling nitrogen composition in streams on the Boreal Plain using genetic adaptive general regression neural networks
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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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 | 0.000 | 0.001 |
| 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 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".