Temporal patterns of greenhouse gas emissions from two small thermokarst lakes in Nunavik, Canada
Bibliographic record
Abstract
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 m2) 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 CO2 (22.1 ± 20.5 mmolm-2d-1; mean ± standard deviation) and CH4 (14.3 ± 14.2 mmolCO2-eqm-2d-1) were consistent with values reported for similar thermokarst lakes, while N2O 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) CO2 fluxes increased by 47 %, CH4 by 95 %, and negative N2O fluxes by 75 % relative to nighttime fluxes. Moreover, ebullitive CH4 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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".