Evaluation of the Potential of Wave Energy in Chile
Bibliographic record
Abstract
In Chile, incentives have been created during the past years for the installation of non-conventional renewable energy plants (NCRE). It is within this context that wave energy can be transformed into a feasible alternative for electrical power generation in the near future within the country. This work corresponds to the first approach to quantify the wave energy resources in Chile based in a technically superior manner. The first step in the assessment of a wave energy plant is to quantify the available resources, therefore the wave climate was obtained for various sites along the Chilean coastline and a deterministic assessment was made of the power of the waves and their main characteristics, especially the variability under different time horizons. In order to convert the mechanical energy of the waves into electrical power, an assessment was made of various offshore devices existing on the market. An estimate was made of the output power of these conversion devices based on the wave climate and on the energy conversion matrixes that define them, performing an analysis that is completely analogue to that of wave power. Waves in the Chilean coast arrive year in and year out with scarce variation during the various seasons, are very regular, with low directional dispersion and high periods. This determines the low seasonal variability of the power and the high capacity factors that conversion devices can develop. The characteristics of waves in Chile are due mainly to the presence of swell usually found in great oceans, which makes Chilean territory one of the most appropriate sites in the world for the generation of electrical power with energy from the waves.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".