Bibliographic record
Abstract
This Ultra Low Sulfur (ULS) Home Heating Oil Demonstration Project was funded by the New York State Energy Research and Development Authority (NYSERDA) and has successfully quantified the environmental and economic benefits of switching to ULS (15 PPM sulfur) heating oil. It advances a prior field study of Low Sulfur (500 ppm sulfur) heating oil funded by NYSERDA and laboratory research conducted by Brookhaven National Laboratory (BNL) and Canadian researchers. The sulfur oxide and particulate matter (PM) emissions are greatly reduced as are boiler cleaning costs through extending cleaning intervals. Both the sulfur oxide and PM emission rates are directly related to the fuel oil sulfur content. The sulfur oxide and PM emission rates approach near-zero levels by switching heating equipment to ULS fuel oil, and these emissions become comparable to heating equipment fired by natural gas. This demonstration project included an in-depth review and analysis of service records for both the ULS and control groups to determine any difference in the service needs for the two groups. The detailed service records for both groups were collected and analyzed and the results were entered into two spreadsheets that enabled a quantitative side-by-side comparison of equipment service for the entire duration of the ULS test project. The service frequency for the ULS and control group were very similar and did indicate increased service frequency for the ULS group. In fact, the service frequency with the ULS group was slightly less (7.5 percent) than the control group. The only exception was that three burner fuel pump required replacement for the ULS group and none were required for the control group.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".