Bibliographic record
Abstract
As the Earth’s surface is mostly covered by water, the sea represents an enormous source of clean energy. This source is so far minimally exploited, but its potential is much larger than the entire global energy demand. This energy can be converted from different forms as tidal, wave, marine current, temperature variation, and salinity, representing an effective resource for most of the planet. Conversion strategies and technologies developed so far have demonstrated the feasibility and the interest in such a large issue, showing the way forward to big challenges to match with. Recent advances in engineering and technology led to more efficient and cost-effective solutions. Different governments have provided funding to research activities regarding the exploitation of marine energy, most notably UK, China, USA, Canada, Sweden, Portugal, Ireland, Spain, Australia, Korea, Nigeria, New Zealand, Mexico, and Japan. As a result of this interest, a large number of devices, exploiting different marine energy components, have been proposed. Many marine energy converters have been patented, while some have already been considered mature for application. The extraordinary variety of the proposed technologies testifies both the vitality of the field and the ingenuity of the researchers in the field. In this special issue authors submitted their original contributions and review articles that cover the main important aspects related to ocean technologies, mainly focusing on wave, ocean current, and tidal energy harvesting. Numerical simulations and experimental validation have been carried out. Several devices have already been built and performance is reported. Efficiency is a key point for this kind of application; thus, a strong effort has been made in order to develop control algorithms and mechanical solutions which improve the performance of the energy conversion. It is now a common opinion that marine energy conversion will be one of the leading research fields at least in the next 15 years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.018 |
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".