Effect of watershed subdivision on water-phase phosphorus modelling: An artificial neural network modelling application
Bibliographic record
Abstract
This study is an effort to incorporate low-cost time-variant remote sensing (RS) information in watershed-scale total phosphorus (TP) modelling. Four watershed subdivisions were delineated to assess the impact of watershed subdivision on the prediction accuracy of TP concentration in stream water. Four TP artificial neural network (ANN) models were designed to incorporate RS data into a semi-distributed approach. The remotely derived enhanced vegetation index and the normalized difference water index were successful in representing vegetation dynamics in the devised models. The models were applied to a 15.6 km 2 watershed in the Canadian Boreal Plain. Eight measures of goodness-of-fit statistics were used for model evaluation. Although statistical model evaluation did favour the finest resolution in this case study, the differences in performance indicators among the four models were insignificant for any practical application. The encouraging results from this exercise demonstrate the applicability of the ANN semi-distributed modelling approach and the usefulness of RS data in simulating TP dynamics. Such models can potentially serve as valuable tools for watershed-scale forest management.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".