Final Design Report for a STEM Exhibit about Green Aviation Fuel Alternatives for the Royal Aviation Museum of Western Canada
Bibliographic record
Abstract
The Royal Aviation Museum of Western Canada (RAMWC) along with industry partners EnviroTREC and WestCaRD, identified the need for a STEM based exhibit focused on sustainable aviation fuels. This initiative aligns with the global shift towards environmentally friendly energy sources, transitioning from convectional fuels like gasoline and kerosene to potential greener alternatives including lithium-ion batteries, biofuel, and liquid hydrogen. Through an interactive, STEM based exhibit, visitors will have the opportunity to learn about these fuel options and their underlying concepts. To ensure a successful exhibit design, several technical specifications and constraints were outlined prior to developing a solution. These included size and accessibility dimensions, performance requirements, inclusion of tactile components, and a significant “wow” factor. These predefined objectives guided the development of the design solution, which is composed of three distinct components that make up the entire exhibit: a historical timeline, a specific energy station, and a CO2 emissions station, each incorporating STEM principles through visual and hands-on experiences. The timeline provides a rich history about the evolution of aviation fuels. For the specific energy station, it allows visitors to gauge the specific energy of different aviation fuels by propelling planes along a track. The emissions station offers an interactive demonstration of CO2 output of various aviation fuels, enabling visitors to visualize and compare emissions through a fog display. The prototype of the conceptual solution was tested against the established specifications to ensure the creation of a successful design. The dimensions of the prototype were tested for public accessibility and passed. Additionally, the exhibit’s content was evaluated for understandability, targeting various comprehension levels to ensure that the STEM principles are clear to the visitors. Using a text scoring system, the prototype achieved a 6th grade reading level of comprehension. “Wow” factor and user friendliness of tactile components were assessed via survey with museum visitors. The feedback indicated that while the exhibit’s design was easy to use and engaging, it could benefit from additional elements to truly inspire and captivate visitors. Following evaluation of the prototype, several enhancements were proposed to the RAMWC that could improve the exhibit’s design. Replacing the chalkboard with a smart touchscreen board would allow for unlimited storage of visitors’ ideas on green aviation fuel, which can also be shared as a display when the screen is idle. Additionally, the exhibit could be optimized for efficiency through changes that include adopting a modular design for easy updates, incorporating a drainage at the emissions station to eliminate mold risk, and utilizing materials that are both lightweight and robust. To improve accessibility, incorporating auditory or tactile feedback would assist those who are visually impaired, and incorporating QR codes could deepen the educational experience. The RAMWC exhibit represents a dynamic fusion of education and innovation, igniting curiosity and fostering learning about sustainable aviation fuels.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.013 |
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".