Predicting the Partial Pressure of Carbon Dioxide in Boreal Lakes
Bibliographic record
Abstract
Sixteen boreal lakes in northern Alberta were sampled for a suite of water chemistry parameters, including dissolved carbon dioxide (CO2), using a headspace gas analysis technique. The lakes encompassed a wide range of pH and alkalinity but had very high dissolved organic carbon (DOC) levels (11–36 mg L−1) and were supersaturated in CO2 with respect to the atmosphere. While the partial pressure of carbon dioxide (pCO2) is regularly estimated from pH and dissolved inorganic carbon (DIC), pH was related to pCO2 at only 13 of 16 lakes and overall pH in combination with DIC was a poor predictor of pCO2. Similarly, despite very high DOC levels, pCO2 was unrelated to the DOC concentration of the lakes. Stepwise multiple linear regressions improved the prediction capability for the entire data set, when compared to simple regressions. Both physicochemical (alkalinity, temperature) and landscape descriptors (lake area, peatland relative area) were important predictors of pCO2. The best regression model included lake area, peatland relative area, and water temperature, and was better able to predict pCO2 than relationships based on DOC, and pH and alkalinity, but lakes with high pCO2 (> 1000 µatm) remain under-predicted and are likely subject to additional factors controlling pCO2 that were not considered in this analysis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".