Sustainable Energy Solutions from Free-Flowing Rivers and Tides
Bibliographic record
Abstract
Ocean Renewable Power Company (ORPC) brings marine renewable energy power systems and project development solutions to its community and industrial partners, specializing in microgrid to utility-scale river and tidal energy applications. ORPC has active projects in Eastport and Millinocket, Maine; Igiugig, Alaska; and Manitoba, Canada. Kaelin Chancey and Liam Pillsbury, both engineers at ORPC and UNH grads, will discuss the unique design and operation associated with ORPC’s innovative underwater power systems. They will also touch on the lab testing of underwater generators using ORPC’s newly fabricated dynamometer test tank at the company’s engineering and electronics laboratory in Brunswick, Maine. Overall, the presentation will cover power systems developed by ORPC along with insights from engineers working on these power systems. Presenter Bio Kaelin Chancey is a mechanical engineer at ORPC and a UNH graduate. With ORPC since 2021, she has focused on the design of ORPC’s generator subsystems and the building of a dynamometer tank for testing of generators at the company’s engineering and electronics laboratory. While at UNH, Kaelin worked on the Living Bridge Project, troubleshooting the operation of the tidal energy conversion system, installing instrumentation, and analyzing flow data. She graduated with a B.S. and M.S. in Mechanical Engineering. Liam Pillsbury’s focus at ORPC is research and development of new technologies and systems including the Modular RivGen® Power System. With a background in design, fabrication, assembly, testing and deployment of mechanical systems, he is an experienced project lead and test lead in multiple at-sea test events. Before joining ORPC, Liam worked for the Naval Undersea Warfare Center as a Department of Defense civilian engineer. He received a B.S. and an M.S. in Ocean Engineering from UNH. In his free time, Liam enjoys spending time on the water, and adventuring with his dog Marlin.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".