3D printing and scanning at the Dalhousie University Libraries: a pilot project
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe how 3D printing and scanning technology was implemented by the Dalhousie University Libraries in Halifax, Nova Scotia. Insights will be outlined about the benefits of these technologies in terms of data visualization and archival practices, as well as the potential user base for library‐centered 3D printing and scanning services. Design/methodology/approach This paper describes why the Dalhousie University Libraries purchased a 3D printer and scanner, the challenges of maintaining these technologies and instructing students in their use, and how Dalhousie faculty members and students have made use of these technologies for their own research purposes. Findings 3D printing and scanning technologies can be of use to a much wider range of Faculties than have traditionally had access to them. The unique role libraries have on university campuses allows them to function as universal access points for these technologies. By offering 3D scanning technology, they can also use this technology internally for archival purposes. Originality/value While much has been written on 3D printing and scanning technology, very little has been written about how these technologies could relate to academic libraries. This paper sets the groundwork for further exploration into how 3D technologies can improve and expand library services.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".