Assessing carbon dynamics in natural and perturbed boreal aquatic systems
Bibliographic record
Abstract
Most natural freshwater lakes are net greenhouse gas (GHG) emitters. Compared to natural systems, human perturbations such as watershed wood harvesting and long‐term reservoir impoundment lead to profound alterations of biogeochemical processes involved in the aquatic cycle of carbon (C). We exploited these anthropogenic alterations to describe the C dynamics in five lakes and two reservoirs from the boreal forest through the analysis of dissolved carbon dioxide (CO2), methane (CH4), oxygen (O2), and organic carbon (DOC), as well as total nitrogen and phosphorus. Dissolved and particulate organic matter, forest soil/litter and leachates, as well as dissolved inorganic carbon were analyzed for elemental and stable isotopic compositions (atomic C:N ratios, δ13Corg, δ13Cinorg and δ15Ntot). We found links between the export of terrestrial organic matter (OM) to these systems and the dissolved CO2 and O2 concentrations in the water column, as well as CO2 fluxes to the atmosphere. All systems were GHG emitters, with greater emissions measured for systems with larger inputs of terrestrial OM. The differences in CO2 concentrations and fluxes appear controlled by bacterial activity in the water column and the sediment. Although we clearly observed differences in the aquatic C cycle between natural and perturbed systems, more work on a larger number of water bodies and encompassing all four seasons should be undertaken to better understand the controls, rates, and spatial as well as temporal variability of GHG emissions, and to make quantitatively meaningful comparisons of GHG emissions (and other key variables) from natural and perturbed systems.
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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 | 0.000 | 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 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".