Natural variability of carbon dioxide and net epilimnetic production in the surface waters of boreal lakes of different sizes
Bibliographic record
Abstract
The variability of surface water carbon dioxide concentration, or partial pressure (pCO2), was studied in 11 lakes of greatly varying size (2.4 ha up to 8 million ha) in Northwest Ontario, Canada. Six of these lakes were chosen to be as similar as possible in all respects except surface area (the Northwest Ontario Lake Size Series [NOLSS], which range from 88 to 35,000 ha). Spatial and temporal variability of pCO2 within a single lake was no greater in the larger lakes than in the smaller lakes. Interannual variability was significant and synchronous, which indicates that weather patterns were important and affected the different lakes within the region in a similar manner. However, annual pCO2 averages were not related to annual differences in planktonic photosynthetic activity, measured by 14CO2 fixation. In the six NOLSS lakes, there was not a significant relationship of average pCO2 with lake size. For all 11 lakes, however, there was a significant negative correlation of pCO2 with lake size, which was likely due to several characteristics of the very small and very large lakes that covaried with size. The larger lakes were deeper and had longer water residence times and lower DOC, which suggests lower CO2 production from allochthonous organic carbon inputs. Also, the ratio of epilimnetic sediment area/epilimnetic volume (Ae/Ve) was smaller in the larger lakes, which likely resulted in lower rates of recycling of fixed carbon to CO2 during summer stratification.
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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 | 0.001 |
| 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".