mercury emissions estimated with a surface emission model
Bibliographic record
Abstract
Most mercury emission inventories only include anthropogenic emissions and neglect the large contribution of the natural mercury cycle due to difficulty in spatially estimating natural emissions and uncertainties in the natural emissions process. The Mercury (Hg) Surface Interface Model (HgSIM) has been developed to estimate the natural emissions of mercury, for inclusion in a more complete mercury emissions inventory. The model used a 3422 cell, 36 km on each side, gridded domain and 1 h time steps. The emissions over land are modeled as a function of the land cover, evapotranspiration, and temperature. The emissions over water are modeled as a function of the concentration gradient, the mixing of the air and water, and the temperature. The spatially distributed model is shown to account for the extreme spatial variability across the Northeast (NE) US and Southeast (SE) Canada. Estimates of natural mercury flux from uncontaminated surfaces are presented for a 2 week period in July. The total natural emissions for the domain, 4,434,912 km 2, was 2101.5 kg over the 2 week simulation. The highest total natural emissions were 820 ng m 2 from the Atlantic Ocean in the SE part of the domain and the lowest total natural emissions were 74 ng m 2 in the urban areas with little vegetation. The flux estimates from vegetation canopies, averaged over the 14 days, ranged from 0.0 ng m 2 h 1 during the night time hours when transpiration ceased to 4.46 ng m 2 h 1 during the afternoon in a mixed deciduous–coniferous forest. The range of the
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".