Neural networks modelling of nitrogen export: model development and application to unmonitored boreal forest watersheds
Bibliographic record
Abstract
In remotely located boreal forest watersheds, monitoring nitrogen (N) export in stream discharge often is not feasible because of high costs and site inaccessibility. Therefore, modelling tools that can predict N export in unmonitored watersheds are urgently needed to support management decisions for these watersheds. The hydrological and biogeochemical processes that regulate N export in streams draining watersheds are complex and not fully understood, which makes artificial neural network (ANN) modelling suitable for such an application. This study developed ANN models to predict N export from watersheds relying only on easily accessible climate data and remote sensing (RS) data from the public domain. The models were able to predict the daily N export (g/km2/d) in five watersheds ranging in size from 5-130 km2 with reasonable accuracy. Similarity indices were developed between any two studied watersheds to quantify watershed similarity and guide the transferability of models from monitored watersheds to unmonitored ones. To demonstrate the applicability of the ANN models to unmonitored watersheds, the calibrated ANN models were used to predict N export in different watersheds (unmonitored watersheds in this perspective) without further calibration. The similarity index based upon a rainfall index, a peatland index and a RS normalized difference water index showed the best correlation with the transferability of the models. This study represents an important first step towards transferring ANN models developed for one watershed to unmonitored watersheds using similarity indices that rely on freely available climate and RS data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".