Alaska Natural Gas Development AuthorityAbstract
Bibliographic record
Abstract
Could propane from Alaska’s North Slope reduce energy costs for electric utilities and residential space heating, water heating, and cooking demands? We explored the hypothesis that propane is a viable alternative for fourteen selected communities along the Yukon and Kuskokwim Rivers, coastal Alaska, and Fairbanks. Our analysis forecasts propane and fuel prices at the wholesale and retail levels by incorporating current transportation margins with recent analysis on Alaska fuel price projections. Annual savings to households associated with converting to propane from fuel oil can be up to $1,700 at $60 per barrel (bbl) of crude oil, and amount to $5,300 at $140 per barrel. 1 Fairbanks residents would benefit from switching to propane for all applications at crude oil prices of $60/bbl. Interesting to note is that switching to propane for domestic water heating makes more sense at lower oil prices than conversions for home space heating. Three of the fourteen communities are projected to benefit from switching to propane for home heating at crude oil prices greater than $80 per barrel, and four communities at crude oil prices of more than $110/bbl. On the other hand, nine communities would benefit from conversion to propane for water heating as crude oil
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.151 | 0.049 |
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".