IDENTIFICATION OF POSSIBLE MERCURY SOURCES AND ESTIMATION OF MERCURY WET DEPOSITION FLUX IN LAKE ONTARIO FROM LAKE ONTARIO ATMOSPHERIC DEPOSITION STUDY (LOADS)
Bibliographic record
Abstract
Total gas phase mercury (TGM) concentrations and event wet-only precipitation for Hg were collected for nine months (from April, 2002 to Dec., 2002) at Sterling, NY on the shoreline of Lake Ontario. TGM concentrations measured in this study () were in somewhat high range compared to other background sites. Using simplified quantitative transport bias analysis (SQTBA) possible sources affecting high Hg concentration in Sterling was identified, and they are coal-fired power plants located in southern NY and Pennsylvania. Wet deposition measured at Mercury Deposition Network (MDN) sites including Pt. Petre and Egbert, ON were compared with data obtained at the Sterling to estimate the total mercury wet deposition flux to Lake Ontario. The wet deposition flux was calculated to be the highest at the Sterling site ( from April, 2002 to Dec. 2002) and the lowest at the Egbert (), due to the both the difference in precipitation depth and Hg concentration in the precipitation. The deposition measured at the Sterling site is similar to Lake Michigan deposition of (converted for ninth months) measured for Lake Michigan Mass Balance Study (LMMBS).
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".