Marine Radar Derived Current Vector Mapping at a Planned Commercial Tidal Stream Turbine Array in the Pentland Firth. Poster presented at: International Conference on Ocean Energy, Halifax, Nova Scotia, Canada, 4-6 Nov 2014
Bibliographic record
Abstract
The first small grid-connected arrays of tidal stream turbines are expected to be deployed in UK waters over the next few years, with MeyGen beginning installation operations in late 2014, and planning electricity generation by 2016. \nUnderstanding the high spatial and temporal variability of currents exhibited at such sites is of critical importance in determining turbine locations in terms of optimising predicted energy generation and device longevity. \nA marine radar was deployed on a remote clifftop overlooking a 4.8km radius area of the Inner Sound of Stroma in the Pentland Firth, Scotland, for 3 months during spring 2013. The area viewed by the radar includes the Crown Estate lease areas for Meygen Ltd (Inner Sound of Stroma) and Scottish Power Renewables (Ness of Duncansby). Data were post processed to extract current vector maps based on determining the Doppler shift of sea surface waves by the tidal current. \nThis same analysis is now running operationally at the European Marine Energy Centre (EMEC) Fall of Warness Tidal Test Site producing current vector maps when there is sufficent sea clutter present.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".