Comment on egusphere-2024-2895
Bibliographic record
Abstract
Abstract. The Canadian Air and Precipitation Monitoring Network (CAPMoN) measures total gaseous mercury (TGM) at three rural-remote sites. Long-term TGM, ancillary measurements and the Positive Matrix Factorization (PMF) model were used to assess temporal changes in anthropogenic and natural surface emission (wildfires plus re-emitted Hg) contributions to TGM and examine the emission drivers of the observed TGM trends between 2005 and 2018. TGM showed decreasing trends at the three sites; the magnitudes (ng m-3 yr-1) were -0.050 at Saturna for 2010–2015, -0.026 at Egbert for 2005–2018, and -0.014 at Kejimkujik for 2005–2016. The increasing contributions from natural surface Hg emissions at Saturna (1.64 % yr-1) and Kejimkujik (1.03 % yr-1) resulted from declining anthropogenic Hg emissions and increasing oceanic and terrestrial Hg re-emissions. The mean relative contributions of natural surface emissions to annual TGM were 65 %, 72.5 % and 65 % at Saturna, Egbert and Kejimkujik. TGM at Saturna were mainly from background Hg (53 %), Hg re-emissions (14 %), and shipping (10 %); at Egbert, from background Hg (63 %), Hg re-emissions (15 %), and crustal/soil dust (9 %); and at Kejimkujik, from background Hg (71 %), regional point source emissions (10 %), and Hg re-emissions (8 %). Local combustion sources contributed a few percent of the annual TGM, while the percentage from oceanic Hg evasion was 6.6–9.5 % for the two coastal sites. Wildfire impacts on annual TGM were 5.6 % at Saturna, 1.3 % at Egbert, and 2.1 % at Kejimkujik. Background Hg contributions to TGM were greater in the cold season, whereas wildfire and surface re-emission contributions can be significant in the warm season.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.022 | 0.011 |
| Insufficient payload (model declined to judge) | 0.396 | 0.289 |
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".