Bibliographic record
Abstract
Securing good results from a retail power contract is more than merely receiving a good price. Depending on a customer's leverage and his awareness of the purchasing process, a variety of other options may be pursued that could improve the bottom line. Ways to do so are reviewed, including some with immediate financial benefit and others that could provide future value were a contract to be renewed or extended. Among the items covered are: use of the customer's name, renewal bonus, size/duration of contract, “swing” allowance and penalties, share of available incentives, account “splitting” and/or triage, and use of interval data. Issues that could impact a customer's price and term leverage are also reviewed, including credit, metering, acceptance of price volatility, load factor/profile, energy services “hunting license,” cross-marketing potential, load curtailability, use of reverse auctions, and prior experience with the vendor. Many of the items covered are taken from the author's online power procurement training course, “Power Techniques for Power Procurement” (for details, go to: www.aeecenter.org/realtime/Power-Purchasing) and come from his first-hand experience serving large retail power customers in both the U.S. and Canada.
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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.009 |
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".