Determine the best location for Ocean Thermal Energy Conversion (OTEC) in Iranian Seas
Bibliographic record
Abstract
Two-thirds of the earth's surface is covered by oceans. These bodies of water are vast reservoirs of renewable energy. Ocean Thermal Energy Conversion technology, known as OTEC, uses the ocean’s natural thermal gradient to generate power. In geographical areas with warm surface water and cold deep water, the temperature difference can be leveraged to drive a steam cycle that turns a turbine and produces power. Warm surface sea water passes through a heat exchanger, vaporizing a low boiling point working fluid to drive a turbine generator, producing electricity. OTEC power plants exploit the difference in temperature between warm surface waters heated by the sun and colder waters found at ocean depths to generate electricity. This process can serve as a base load power generation system that produces a significant amount of renewable, non-polluting power, available 24 hours a day, seven days a week. In this paper investigated the potential of capturing electricity from water thermal energy in Iranian seas (Caspian Sea, Persian Gulf and Oman Sea). According to the investigated parameters of OTEC in case study areas, the most suitable point in Caspian Sea for capturing the heat energy of water is the south part of it which is in the neighborhood of Iran and the most suitable point in the south water of Iran, is the Chahbahar port.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".