Technoeconomic data adopted for the development of a long-term electricity supply model for the Hashmite Kingdome of Jordan
Bibliographic record
Abstract
Electrical generation in Jordan currently relies on imported fossil fuels. In the past, most imported fossil fuels were subsidised by neighbouring countries through grants and aid. This has led to a regulated market, with subsidised low-cost electrical energy consumers, and the government being the sole buyer and seller of electricity. With the ageing of the national electrical infrastructure, political instability in the region, and lack of funds for direct investment, other options needed to be pursued. Long term Power Purchase Agreements (PPA) were granted to Independent Power Producers (IPP) to encourage investment in capacity and infrastructure. In addition, long-term fuel contracts were signed to secure steady flow of primary fuel sources. Over the past few years, renewable energy penetration has increased rapidly, but without proper planning or taking into consideration long term PPA and fuel contracts. Data in regard to the current infrastructure, renewable energy technology, signed energy commitments and system operation assumptions are described in this article, which may be used for modelling and analysis. The Data were collected from annual reports from the different energy related entities in Jordan.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".