Assessment of wave energy in the Persian Gulf: An evaluation of the impacts of climate change
Bibliographic record
Abstract
We are motivated to study the exploitation of marine energy as a renewable resource because of society's ever-increasing energy demands, and a concomitant need to reduce greenhouse gas emissions. Additionally, climate-related variations in wave energy should be investigated in order to ensure the stability of its long-term availability. Here, we investigate the potential for wave energy in the Persian Gulf along the southern coasts of Iran. To do so, we have applied the Mike SW numerical model and ECMWF wind field data for a 30-year study, from 1988 to 2017. For this purpose, wave energy was evaluated at six points in the western, northern, southern, and eastern parts of the Persian Gulf. To assess the impacts of climate change, we also consider the wave regime from 2070 to 2099 (for 30 years) following IPCC RCP4.5 and RCP8.5 climate change scenarios. Our findings suggest that in the present climate, seasonal variations in the mean wave parameters (i.e. wave energy, wave period, and significant wave height) correspond to the lowest wave energy in the summers, and highest in the winters. In the future climate change scenarios, energy level variations generally have similar patterns, with slight modulations in some local areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".