Generalized Additive Models of Climatic and Metabolic Controls of Subannual Variation in pCO<sub>2</sub> in Productive Hardwater Lakes
Bibliographic record
Abstract
Abstract Spatiotemporal variation in climate and weather, allochthonous carbon loads, and autochthonous factors such as lake metabolism (photosynthesis and respiration) interacts to regulate atmospheric CO2 exchange of lakes. Understanding this interplay in diverse basin types at different timescales is required to adequately place lakes into the global carbon cycle and predict CO2 flux through space and time. We analyzed 18 years of data from seven moderately hard lakes in an agricultural prairie landscape in central Canada. We applied generalized additive models and sensitivity analyses to evaluate the roles of metabolic and climatic drivers in regulating CO2 flux at the intra‐annual scale. At mean conditions with respect to other predictors, metabolic controls resulted in uptake of atmospheric CO2 when surface waters exhibited moderate primary production but released CO2 only when primary production was very low (<8 μg/L or when dissolved nitrogen was elevated (>2,000 μg/L), implying that respiratory controls offset photosynthetic CO2 uptake under these conditions. Climatically, dry conditions increased the likelihood of in‐gassing, likely due to evaporative concentration of base cations and/or reduced allochthonous carbon loads. While more research is required to establish the relative importance of climate and metabolism at other timescales (diel, autumn/winter), we conclude that these hard fresh waters characteristic of continental interiors are mainly affected by metabolic drivers of pCO2 at daily‐monthly timescales, are climatically controlled at interannual intervals, and are more likely to in‐gas CO2 for a given level of algal abundance than are soft water, boreal ecosystems.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".