Bibliographic record
Abstract
The global energy demand has led to increased oil production across the world, leading to an increase in the number of offshore oil platforms and oil tanker traffic. Further, this demand has pushed the industry to develop in increasingly remote areas and harsh environments. Newfoundland, which operates the largest offshore oil platform in the world, has a strong safety record, but limited disaster response capabilities. The Newfoundland ecosystem is particularly sensitive to the effects of an oil spill, with its diverse marine life, rugged coastline, and harsh climate. Further, these harsh conditions reduce the effectiveness of standard containment and recovery options for oil spills used elsewhere. The author seeks to address the issue by designing a deployable oil dispersant system for fixed wing aircraft. Oil dispersants sprayed onto the surface of an oil slick remain one of the most effective methods for mitigating the effects of an oil spill. The primary focus of this thesis is the preliminary engineering design of the system, which includes the use of theoretical and computational stress analysis techniques, aerodynamics, and rigid body dynamics considering the motion of the system. The thesis concludes with the preliminary system design of a deployable oil dispersant system that is adaptable to multiple aircraft platforms.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.005 | 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".