Spatiotemporal Variability in Lake‐Atmosphere Net CO<sub>2</sub> Exchange in the Littoral Zone of an Oligotrophic Lake
Bibliographic record
Abstract
Abstract Lakes may function as either sinks or sources of CO2. Their response to climate change is uncertain, as we lack continuous data of lake CO2 efflux and its drivers. This is especially true in the littoral zone of lakes, which can be very dynamic from the continuous injection and remobilization of terrestrial nutrients. This study used high‐frequency measurements of CO2 exchange during the ice‐free season by prototype low‐power floating forced diffusion autochambers. We quantified the net surface flux of CO2 across a transect of the littoral zone of a small deep oligotrophic lake in eastern Nova Scotia, Canada, and examined potential drivers. The littoral zone was a net source for CO2, on average emitting 0.171 ± 0.023 μmol CO2 · m−2 · s−1, but we did observe significant temporal variation across diel and seasonal periods, as well as with distance from shore. While no pelagic environmental driver appeared to explain this variability in CO2 exchange, our study suggests that factors that vary on a fine spatial scale within the littoral zone may effectively regulate CO2 exchange. If environmental drivers of pelagic CO2 exchange are unrelated to CO2 exchange in the littoral zone, this may have large implications for current mechanistic understandings of lake carbon dynamics and for upscalings of fluxes. This work shows the spatial and temporal variability of littoral CO2 efflux, as well as the utility of low‐power forced diffusion automated chambers for observing lake‐atmosphere net CO2 exchange.
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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 | 0.000 | 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) | 0.000 | 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".