Spatial variability in terrestrial and aquatic carbon stocks and fluxes in boreal forested catchments
Bibliographic record
Abstract
Canada’s boreal zone is a complex mosaic of forests, wetlands, streams and lakes. The pool of carbon (C) stored in each of these ecosystem components is vast, and significant to the global C balance. However, C pools and fluxes are heterogeneous in time and space, which contributes to uncertainty in predicting how a changing climate will affect the fate of C in these sensitive ecosystems. The objective of this study was to investigate factors controlling spatial variability in soil C stocks and stream C export and assess the sensitivity of these stocks and fluxes to climatic factors. We conducted a detailed examination of soil C stocks and stream dissolved organic C (DOC) export from a 320 ha boreal forested catchment located in northwestern Ontario, Canada. High-frequency stream chemistry and discharge samples were collected from three inflow streams during snowmelt and rain events from 2016-2017. An intensive soil C sampling campaign resulting in 47 surface (0 – 30 cm) samples were collected during the summer of 2019. Stream hysteresis analysis revealed marked differences in flowpaths among sub-catchments during snowmelt and rain events. In the wetland-dominated catchment, near-stream sources contributed most of the DOC export during both rainstorms and snowmelt events, but in upland-dominated catchments, the sources of DOC depended on antecedent moisture conditions. Rainstorms in these catchments following prolonged droughts resulted in DOC flushing from distal regions of the catchment. Soil C stocks were also highly spatially variable, with much of the variability being explained by local-scale factors (e.g. gravel content, soil depth, distance to the nearest ridge). Taken together, these two findings emphasize the need to consider sub-catchment scale variability when calculating C pools and fluxes in boreal catchments. This is also important when predicting how C dynamics will shift in the future as a result of shorter winters, longer droughts and more intense rainstorms.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".