Seasonality of pCO<sub>2</sub> in a hard‐water lake of the northern Great Plains: The legacy effects of climate and limnological conditions over 36 years
Bibliographic record
Abstract
Abstract Biogeochemical processes are active year‐round in ice‐covered lakes, such that processes in one season can affect limnological conditions in subsequent seasons. However, the extent and nature of these legacy effects are poorly understood, particularly for the CO2 content of lakes and when considering gas exchange with the atmosphere. Here, we used a unique 36‐yr dataset of weekly limnological measurements of Buffalo Pound Lake in the northern Great Plains to assess seasonal changes in CO2 concentration and flux and determine how dependent lake pCO2 is on limnological conditions of previous seasons. We found that the lake was a net source of CO2 to the atmosphere (mean 18.5 ± 7.4 mol CO2 m−2 yr−1), with spring potentially accounting for the majority (~ 64% ± 20%) of CO2 efflux, assuming ice in spring was permeable to gas exchange (32.9% ± 19.8% if not). Analysis with generalized additive models (GAMs) demonstrated that current and antecedent seasonal conditions combined to explain 72.6% of deviance in spring pCO2, but that the strength of model predictions and the importance of antecedent conditions diminished in GAMs of summer (43.6%) and fall (23.3%) CO2 levels. This research suggests that pCO2 is regulated by a combination of coeval and historical environmental conditions and shows that quantification of seasonal and annual fluxes requires a mechanistic understanding of the legacy effects of preceding time intervals.
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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 | 0.001 |
| 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) | 0.001 | 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".