Bibliographic record
Abstract
This project began when, seeking a self-sponsored project, we decided to pursue the concept of open-source hardware. While searching for a project to develop, we happened upon the RepRap 3D printer. The potential to close the gap between software and hardware that a 3D printer could offer was apparent, and we immediately set out in search of ways to improve the device. Thus began what would become the SpoolHead project, but we three may not claim all of the credit. A large share of the credit goes to the inventors of the RepRap itself, primarily Adrian Bowyer and Ed Sells, but also many other contributors. We also should thank Sebastien Bailard for promoting our project and helping us manage documentation. All of our work on the RepRap would never have been possible if we had not come across Wade Bortz, an inventive and unbelievably generous Vancouver RepRap developer who offered to print us a full set of Darwin parts. When we found that our extruder didn’t have enough torque, Wade gave us one of his own geared extruders to “test out”. He patiently bore our nagging questions and bicycled all the way to the University of British Columbia campus to attend our presentations. Wade represents the best example of how a community
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.419 | 0.305 |
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".