Bibliographic record
Abstract
The Power Wall Project is a custom-piece of power equipment designed to provide an electrical source, on-demand when the equipment is away from standard electrical outlet. The team of Islay Clare-Kellet (Power) and Katharyn Taylor (Telecom) worked closely together to design, procure materials and components, create detailed design, build, test, commission, and document the project. The overall cost for the project materials went over budget of $800 by $200 due to replacement of equipment during the commissioning phase. During the seven phases of the project plan, the team experienced accomplishments, like completing the working version of the Power Wall. The first prototype the current Power Wall performs well and provides three voltage levels and can be started and stopped simply with the two push buttons and latching relays. The physical unit is can easily be used a camping power supply and it isn’t the typical and polluting generator one can find at their local Canadian Tire story. The finished unit is rugged and can be rolled over rough terrain. It will prove to be an excellent camping companion when needing to power phones, coffee makers or lights. The team also experienced a number of challenges on the technical and human resource sides. On the technical side, the team learned the timing and requirements to carry out in-depth research on limits, capabilities and common issues related to the equipment and processes associated with the design and production of the equipment. Should we have conducted more research, we would have discovered that there was a common issue with turning the analog signal from AM712 to a digital sensor. We also discovered, the hard way, that giving too much voltage to the input pins would kill the pi. In the end, though we were able to construct a working prototype of the Power Wall, with power monitoring capabilities, we learned important lessons on being accountable to the Project Plan deadlines, on team communication and on personal commitment to the project objectives. While these human resource challenges almost seemed insurmountable during the project implementation, it was our perseverance and our team commitment that kept us motivated and focused on the end goal. We are proud of our work and thankful for the experience.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.545 | 0.285 |
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".