Aquatic carbon and GHG export from a permafrost catchment; identifying source areas and primary flow paths
Bibliographic record
Abstract
The aquatic pathway is increasingly being recognized as an important component of landscape scale greenhouse gas (GHG) budgets. Due to low temperatures and short residence times limiting in-stream production in northern headwater catchments, much of the exported carbon is likely to be allochthonous, transported via throughflow to the surface drainage system. Identifying sources and primary flow pathways is therefore essential in understanding and predicting changes in the aquatic flux magnitude. Arctic landscapes are now widely recognised as being particularly vulnerable to climate driven changes. The HYDRA project (“Permafrost catchments in transition: hydrological controls on carbon cycling and greenhouse gas budgets”) aims to understand the fundamental role that hydrological processes play in regulating landscape-scale carbon fluxes, and predict how changes in vegetation and active layer depth in permafrost environments influence the delivery and export of aquatic carbon. \nIn this study we present aquatic concentrations and fluxes of carbon and GHG species collected across two field seasons (2013, 2014) from an arctic headwater catchment in northern Canada. Measured species include dissolved organic (DOC) and inorganic carbon (DIC), CO2, CH4 and N2O. Measurements were made across a range of freshwater types within the tundra landscape, including lakes, ice-wedge polygons, and the ‘Siksik’ stream which drains the (c.a. 1 km2) primary study catchment. A nested sub-catchment approach was used along the ‘Siksik’ stream; ‘snapshot’ sampling of eight points along the stream length allowed specific vegetation communities to be targeted to assess individually their contribution to aquatic export. A combination of stable isotopes and major ion concentrations measured at each sampling point provide additional information to trace source areas and flow paths within the main study catchment. \nCatchment scale evasion and downstream export were calculated and an initial comparison between the relative importance of different water body types presented.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".