CaLNG : Peak Shaving to Alleviate a Supply-Demand Bottleneck
Bibliographic record
Abstract
This case features Isabella Couchet, the chief operations officer of CaLNG, a company that planned to sell liquefied natural gas (LNG) to help California utilities better match supply and demand through peak shaving. The price of natural gas drawn from the California pipeline infrastructure increased with sudden huge demand spikes during the summer and winter peaks, so the ability to use LNG to fulfill demand during peak periods would be a significant financial benefit to utilities. CaLNG planned to receive LNG at its Coos Bay terminal in Oregon and then transport it to California using specialized trailers. It had to design its LNG supply chain while considering the costs of storage facilities and transportation. CaLNG could build a centralized tank farm at Coos Bay and, from there, use a large number of trailers for on-demand delivery. Alternatively, the company could build satellite tanks at utilities, an option that would require fewer trailers because the satellite tanks could be filled during off-peak periods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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".