Large‐Scale Landscape Drivers of CO<sub>2</sub>, CH<sub>4</sub>, DOC, and DIC in Boreal River Networks
Bibliographic record
Abstract
The boreal biome is characterized by extremely dense and complex fluvial networks that are closely coupled to land. Reconstructing the role that these fluvial networks play in regional carbon (C) budgets requires identifying landscape and environmental drivers of riverine C that operate at the whole network scale and that can be applied across landscapes. Here we explore drivers of CO 2 , CH 4 , and dissolved organic (DOC) and inorganic C (DIC) across 190 streams and rivers spanning 8 Strahler orders over an area of 500,000 km 2 of heterogeneous boreal landscape in Québec, focusing on those drivers that can be readily obtained from remote sensing data. Each C species (except DIC) could be modeled as a function of a proximal network‐scale property, such as flow distance or elevation, but adding regional structure to these models greatly improved predictions. These modeled regional effects were similar for DOC, CO 2 , and CH 4 and were strongly related to average regional soil organic content and especially to NPP, the latter integrating regional differences in climate and other environmental factors. These results suggest that there may be regional C baselines determined by a combination of landscape and climate features, which simultaneously influence the average CO 2 , DOC, and CH 4 within fluvial networks, albeit through different underlying mechanisms and with varying degrees of influence on each C species. The latter two C species appear to be more sensitive to regional differences in soil, NPP, and climate than CO 2 or DIC, and therefore more likely to shift under future scenarios of change in northern landscapes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 teacher head, 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".