Artificial neural networks and time series modelling of TP concentration in boreal streams: a comparative approach
Bibliographic record
Abstract
Increased nutrient concentrations in fresh waters may lead to depressed dissolved oxygen concentrations, increased cyanobacterial biomass, and potentially high levels of cyanobacterial toxin production. Phosphorus loading, during snow melt and storm events, is the main source of nutrient enrichment to water bodies on the Canadian Boreal Plain. This study compared two approaches for modelling total phosphorus (TP) concentration: autoregressive moving average with exogenous input (ARMAX) and artificial neural network (ANN) models. Derived models were applied to a small forested watershed on the Canadian Boreal Plain. Results showed that ANN outperformed ARMAX based on four measures of goodness-of-fit statistics. This study confirmed that the ANN modelling approach is superior to ARMAX in modelling time-correlated gapped data, provided step-by-step guidelines for modelling time-correlated variables, and presented a feasible alternative for modelling diffuse pollutants in small forested watersheds. Key words: watershed, snow melt, diffuse pollutants, phosphorus, artificial neural networks (ANN), time series (TS), multi-slab hidden layer, ARMAX, boreal forest.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".