An approach estimating bidirectional air‐surface exchange for gaseous elemental mercury at AMNet sites
Bibliographic record
Abstract
Abstract The bidirectional air‐surface exchange for gaseous elemental mercury (GEM) and existing measurements of the compensation points over a variety of canopy types are reviewed. Deposition and emission of GEM are dependent on several factors such as the type of canopy, temperature, season, atmospheric GEM concentrations, and meteorological conditions, with compensation points varying between 0.5 and 33 ng m−3. Emissions tend to increase from the spring to summer seasons, as the GEM accumulates in the foliage of the vegetation. A strong dependence on solar radiation has been observed, with higher emissions under light conditions. A bidirectional air‐surface exchange flux model is proposed for estimating GEM fluxes at a two‐hourly time resolution for the National Atmospheric Deposition Program's, Atmospheric Mercury Network (AMNet) sites. Compared to the unidirectional dry deposition model used in Zhang et al. (2012), two additional parameters, stomatal and soil emission potential, were needed in the bidirectional model and were chosen based on knowledge gained in the literature review and model sensitivity test results. Application of this bidirectional model to AMNet sites have produced annual net deposition fluxes comparable to those estimated in Zhang et al. (2012) at the majority of the sites. In this study, the net GEM dry deposition has been estimated separately for each dominant land use type surrounding each site, and this approach is also recommended for future calculations for easy application of the results to assessments of the mercury effects on various ecosystems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".