Suspended sediment dynamics in a tributary of the Saint John River, New Brunswick
Bibliographic record
Abstract
Historical hydrometeorological and suspended sediment concentration (SSC) data from the Kennebecasis River, a tributary of the Saint John River in New Brunswick, Canada, were investigated to help understand what drives high sediment transport in that system. Analysis of correlation coefficients between SSC and potential drivers at various time steps suggested that multiple regressions would not be optimal for this purpose, and that lagged flow (Q) and precipitation should be taken into account in any model. A frequency analysis involving annual maxima of SSC, Q, and precipitation events revealed there is no systematic unique driver of extreme annual SSC or high annual loads. Finally, artificial neural network (ANN) models were developed to verify whether the variables examined previously would yield better results in a nonlinear context. Network inputs were mean temperature, Q, Q(t–1), Q(t–2), and day-of-year. Using daily loads directly as a target in the network yielded satisfactory results, with 88% of the variance explained by the model and a mean absolute deviation between estimated and real annual loads of 16%. The ANN model systematically outperformed multiple linear regressions.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".