Assessing inter-annual and seasonal patterns of DOC and DOM quality across a complex alpine watershed underlain by discontinuous permafrost in Yukon, Canada
Bibliographic record
Abstract
Abstract. High latitude environments store approximately half of the global organic carbon pool in peatlands, organic soils and permafrost while large arctic rivers convey an estimated 18–50 Tg C a−1 to the Arctic Ocean. Warming trends associated with climate change affect dissolved organic carbon (DOC) export from terrestrial to riverine environments. However, there is limited consensus as to whether exports will increase or decrease due to complex interactions between climate, soils, vegetation, and associated production, mobilization and transport processes. A large body of research has focused on large river system DOC and DOM lability and observed trends conserved across years, whereas investigation at smaller watershed scales show that thermokarst and fire have a transient impact on hydrologically-mediated solute transport. This study, located in the Wolf Creek Research Basin situated ~ 20 km south of Whitehorse, YT, Canada, utilises a nested design to assess seasonal and annual patterns of DOC and DOM composition across diverse landscape types (headwater, wetland, lake) and watershed scales. Peak DOC concentration and export occurred during freshet per most northern watersheds, however, peaks were lower than a decade ago at the headwater site Granger Creek. DOM composition was most variable during freshet with high A254, SUVA254 and low FI and BIX. DOM composition was relatively insensitive to flow variation during summer and fall. The influence of increasing watershed scale and downstream mixing of landscape contributions was an overall dampening of DOC concentrations and optical indices with increasing groundwater contribution. Forecasted vegetation shifts, permafrost thaw and other changes due to climate change may alter DOM sources from predominantly organic soils to decomposing vegetation, and facilitate transport through deeper flow pathways with an enhanced groundwater role.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".