Sources of variability in mercury flux measurements
Bibliographic record
Abstract
Chamber and micrometeorological mercury flux data collected during the Nevada STORMS intercomparison study were used to identify natural and methodological factors controlling data variability. Micrometeorological and chamber measurements revealed that flux variability at a site is closely related to the Hg concentrations in the substrate, which were found to vary with mineral composition, grain size, and sampling depth. Environmental factors also influenced flux variability. Following two rainfall events, fluxes measured by chamber and micrometeorological methods increased substantially. The micrometeorological flux was enhanced five fold following the rain event. Fluxes measured by both methods were also influenced by net radiation and temperature as evidenced by their tendency to follow the diel cycle in these variables. Daytime fluxes were 6 times greater than nighttime fluxes. Data analysis revealed that interactions between environmental and geochemical variables complicate relationships between the flux and these variables. Understanding the variability at a flux monitoring site is important to establish relationships for scaling up and for the development of consistent sampling protocols that allow comparisons from one study to another and adequately quantify mercury fluxes from natural sites to provide representative emission data that can be used for scaling up to regional and global scales.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".