Bibliographic record
Abstract
Glas Ocean Electric (GOE) embarked on a project to demonstrate the benefits of electric propulsion systems in vessels by retrofitting the Sea Cucumber, a 29-foot Cape Islander fishing boat with an drop-in electric propulsion system. This initiative aimed to highlight the efficiency, performance, and environmental benefits of GOE's Zero Emission Propulsion System (ZEPS). The conversion involved finalizing the design of GOE’s version 2 electric propulsion system, fabricating and assembling the electric propulsion system into a kit, installing the propulsion system kit with an electric motor alongside the existing diesel engine and enabling the vessel to operate on electric propulsion during specific fishing operations and diesel during high-speed transits. The electric system was designed to reduce fuel consumption and emissions significantly during typical fishing operations, where slower speeds are required. Testing was conducted in the Halifax Northwest Arm to generate power curves and compare the ZEPS to traditional diesel propulsion systems. The project included activities emissions testing, which demonstrated that the Battery electric hybrid vessel could eliminate 509 kg of CO2 emission per fishing day if operating on 100% electric and showed a 296 kg reduction in CO2 when only utilizing diesel for high speed transiting, resulting in a 58% emission reduction. GOE also engaged Lloyds Register for an Approval in Principle (AiP) and TC MSS to ensure the system met industry standards and TP 13585 E: Tier I - Policy – Accepting alternative electrical standards for small electric and hybrid vessels.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".