Temporal patterns of greenhouse gas emissions from two small thermokarst lakes in Nunavik, Canada
Bibliographic record
Abstract
Small thermokarst lakes, formed by the thawing of ice-rich permafrost, are significant sources of greenhouse gases (GHGs). Most estimates of emissions rely solely on daily measurements, which may bias annual flux calculations. In this study, we combined GHG flux measurements from two intensive summer campaigns with nearly 2 years of continuous temperature, oxygen and conductivity profiling in two small (< 200 m 2 ) thermokarst lakes in Nunavik (56°33 ′ 28.8 ′′ N, 76°28 ′ 46.5 ′′ W), Canada. One campaign occurred during a colder period (8.8 °C average temperature) and the other during a warmer one (14.6 °C average temperature), with one lake being humic and sheltered and the other more transparent and wind-exposed. Average diffusive fluxes of CO 2 (22.1 ± 20.5 mmolm-2d-1; mean ± standard deviation) and CH 4 (14.3 ± 14.2 mmolCO2-eqm-2d-1) were consistent with values reported for similar thermokarst lakes, while N 2 O fluxes were negligible (−0.8 ± 1.3 mmolCO2-eqm-2d-1). Emissions increased fourfold during the warmer summer, alongside the emergence of a diel trend, where daytime (09:00–17:00 EST) CO 2 fluxes increased by 47 %, CH 4 by 95 %, and negative N 2 O fluxes by 75 % relative to nighttime fluxes. Moreover, ebullitive CH 4 fluxes were six times higher than diffusive fluxes in the humic, sheltered lake, reaching 117.0 ± 44.7 mmolCO2-eqm-2d-1. Seasonal flux estimates indicate that emissions could peak in autumn and spring, as the lakes accumulated large concentrations of GHG at the bottom. Our findings highlight the importance of including both daytime and nighttime measurements, as well as storage fluxes (emitted in spring and autumn), to improve the accuracy of GHG emission estimates from thermokarst lakes.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".