The ecosystem size and shape dependence of gas transfer velocity versus wind speed relationships in lakes
Bibliographic record
Abstract
Air–water diffusive gas flux is commonly determined using measurements of gas concentrations and an estimate of gas transfer velocity (k 600 ) usually derived from wind speed. The great heterogeneity of aquatic systems raises questions about the appropriateness of using a single wind-based model to predict k 600 in all aquatic systems. Theoretical considerations suggest that wind speed to k 600 relationships should instead be system-specific. Using data collected from aquatic systems of different sizes, we show that k 600 is related to fetch and other measures of ecosystem size. Lake area together with wind speed provided the best predictive model of gas transfer velocity and explained 68% of the variability in individual k 600 measurements. For a moderate wind speed of 5 m·s −1 , predicted k 600 varied from 6 cm·h −1 in a small 1 ha lake to over 13 cm·h −1 in a 100 km 2 system. Wave height is also shown to be a promising integrative predictor variable. The modulating influence of system size on wind speed – gas transfer velocity relationships can have a large impact on upscaling exercises of gas exchange at the whole landscape level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".