Bibliographic record
Abstract
Many different technologies have been developed to convert waves into electricity. Two of the most promising technologies take advantage of the vertical motion of waves. The first of these is a buoy or point-absorber generator. These designs contain a fixed component and a floating component. Waves move the floating component up and down in relation to the fixed component, driving one of several types of systems. An arm protruding from the buoy can be attached to a crank, which then turns a mechanical generator. Similarly, self-contained hydraulic pumps can be driven by the motion of the buoy, then driving a hydraulic motor. Yet another system uses the motion to pump pressurized sea water. This pressurized sea water can then be pumped through a turbine or even pumped onshore to drive osmotic desalination processes. Buoy generators are currently being used in several locations. Finavera has projects in waters off Portugal, Africa, and the North Pacific waters of the US and Canada. Oregon State University has a pilot project off the coast of Reedsport, and CETO, has a project running off Western Australia. The second type of design that takes advantage of vertical motion is called an attenuator, also known as surface-following technology. Pelamis devices have cornered this section of the market, and virtually no other technologies are available. These generators derive their name from Pelamis platuris, a yellow-bellied sea snake, a fitting name considering the generator's long, narrow design, and its oscillating movements. The machine consists of long, buoyant tubes connected by two arms at movable joints. As the waves change the angle of two tubes with respect to each other, hydraulic pumps are compressed and stretched, driving hydraulic generators. These Pelamis generators are being used in the world's first commercial wave farm, the Aguçadora Wave Park off Portugal, and also in the 3MW wave farm off the coast of Scotland.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.251 | 0.092 |
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".