Predicting DOC Concentration in the Peel River with a Mechanistic Numerical Model
Bibliographic record
Abstract
Arctic warming is causing increased export of sediments and organic matter via active\nlayer deepening and thermokarst slumps. A mechanistic numerical model was developed\nusing the ReacTran R package to predict riverine dissolved organic carbon (DOC) and total\nsuspended sediment (TSS) concentrations measured during a 2019 field expedition in the\nPeel River watershed, YT, Canada. In addition to advective transport, two geochemical\nDOC removal processes were implemented (DOC mineralization and adsorption to mineral\nsurfaces). The power of upstream slump affected area to predict riverine DOC and TSS\nconcentrations was also investigated via a random forest classifier used to identify slump\nfeatures in the landscape. However, other landscape properties (NDVI, NDMI) proved\nto be better predictors of riverine DOC and TSS, possibly due to inaccuracies in the\nclassification. Steady state model results indicate that 70–90 % of total DOC input to the\nriver was exported from the downstream boundary unaffected by removal processes, and\nthe 10–30 % of input DOC that was removed was done so predominantly via adsorption\nto mineral surfaces. Adsorption was driven by high TSS tributaries entering the model\ndomain in its downstream reaches, with the high TSS values possibly due to increased\nslumping activity in the watersheds of these tributaries. Requisite sensitivity analyses were\nnot performed and offer opportunities for continuation of this work, as does expanding\nthe model to include dynamic inputs and splitting the bulk DOM pool into contributing\ncomponents.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".