Sea-Floor Power Generation System
Bibliographic record
Abstract
Ocean currents represent a potentially significant, \ncurrently untapped, resource of energy. The total worldwide power \nin ocean currents has been estimated to be about 5,000 GW, with \npower densities of up to 15 kW/m2. In the paper we describe a micro \nsea-floor power generation system being designed and developed at \nElectrical Energy System Lab, Memorial University of \nNewfoundland. The ocean current data around Newfoundland \nshows a significantly low non-tidal ocean current speed (in the \nrange of 0.11-0.15m/s) at various depths. We would like to extract \nfew watts electrical from the sea-floor ocean current. The produced \npower is required for the data processing computer and signal \nconditioning circuits of sea-floor instrumentation. The proposed \npower generation system will consist of a spiral shape drag type \nturbine rotor, a low rpm generator, batteries for energy storage, a \ncontrolled DC-DC converter, instrumentation and a micro \ncontroller based control system for the turbine. This paper describes \nprogress made so far. We present some ocean current data, design \nand lab test results of our first proto-type sea-floor power generation \nsystem.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.065 | 0.037 |
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".