Bibliographic record
Abstract
This article discusses that in the quest for renewable energy, the oceans’ tides and flow have gone largely untapped. Companies in the United Kingdom and Canada are trying to harvest the power of sea current through new application of an old technology: turbines. IT Power is using technology from its spin-off company, Marine Current Turbines, also in Hampshire. The technology consists of a pair of axial flow rotors that are roughly 50 to 65 feet in diameter. Each drives a generator via a gearbox, much like a wind turbine. Blue Energy Canada is also working the currents. Its approach differs from that of IT Power in two significant ways: orientation of the turbine blades and their arrangement. A study conducted in 2001 by Triton Consultants, based in Vancouver, BC, on behalf of BC Hydro (one of the largest electrical utilities in Canada), found that the cost to develop a current turbine site is rather high, but the cost of annual power generation would be low. The study considered a site at the Discovery Passage in British Columbia, which it speculated would run 7941-MW Marine Current Turbines spread over roughly 3922 acres.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.117 | 0.035 |
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".