Continental‐scale variation in controls of summer CO<sub>2</sub> in United States lakes
Bibliographic record
Abstract
Abstract Understanding the broad‐scale response of lake CO2 dynamics to global change is challenging because the relative importance of different controls of surface water CO2 is not known across broad geographic extents. Using geostatistical analyses of 1080 lakes in the conterminous United States, we found that lake partial pressure of CO2 (pCO2) was controlled by different chemical and biological factors related to inputs and losses of CO2 along climate, topography, geomorphology, and land use gradients. Despite weak spatial patterns in pCO2 across the study extent, there were strong regional patterns in the pCO2 driver‐response relationships, i.e., in pCO2 “regulation.” Because relationships between lake CO2 and its predictors varied spatially, global models performed poorly in explaining the variability in CO2 for U.S. lakes. The geographically varying driver‐response relationships of lake pCO2 reflected major landscape gradients across the study extent and pointed to the importance of regional‐scale variation in pCO2 regulation. These results indicate a higher level of organization for these physically disconnected systems than previously thought and suggest that changes in climate and land use could induce shifts in the main pathways that determine the role of lakes as sources and sinks of atmospheric CO2.
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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".