DEVELOPMENT OF AN IN SITU INSTRUMENT FOR MEASURING MERCURY IN A GAS STREAM
Bibliographic record
Abstract
As part of its overall Environmental Management Program, the U.S. Department of Energy (DOE) has developed thermal and incineration processes for treating hazardous mixed wastes. These mixed wastes often contain mercury that is released into the atmosphere during the incineration process. The U.S. Environmental Protection Agency (EPA), as well as many states, clearly views mercury emissions from incinerators and combustion systems as a potential human health problem (1). Although validated batch measurement methods such as EPA Method 29, the Ontario Hydro mercury speciation method, and EPA Method 101A exist for total and speciated mercury, they are difficult and costly to perform. In addition, the data are not available for use until several days later. Continuous emission monitors (CEMs) are a very attractive option because the data are in near real-time, allowing the data to be used as feedback control for mercury control strategies. Also, a properly designed analyzer should require minimal operator input. However, based on the current state of the art, mercury CEMs are not without problems, as demonstrated in recent field tests (2). In addition, they are often bulky and costly to purchase. Sensor Research and Development Corporation (SRD) was contracted by DOE's National Energy Technology Laboratory (formerly the Federal Energy Technology Center [FETC]) to develop a prototype instrument for thermal treatment process continuous emission monitoring applications. The work by SRD for DOE on the mercury CEM was conducted under two different contracts. Work under the first contract began October 1, 1997, and ended June 2000, with a second contract continuing until June 2001. The SRD process has the potential to be the basis for a very low-cost mercury CEM. The initial cost estimates provided by SRD are an order of magnitude lower than any other proposed mercury CEM. Although the instrument will be low-cost, it still has the potential to detect low limits of mercury and, therefore, has a wide range of possible uses. In addition, the instrument will be very portable.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".