Bibliographic record
Abstract
Introduction: Collection inventories are time consuming but necessary to clean up catalogue records and improve access and retrieval. This article outlines the methods of carrying out an inventory project at the Dentistry Library, University of Toronto, for the first time in 16 years. As a result, a kit was developed to help implement this project in future years. Description: The kit outlines the steps for the inventory including creating a shelf-list using SIRSIDynix Symphony 3.0's report function, importing into Excel, and separating the collection in smaller sections to make the process less onerous. Outcomes: Readers are informed of the results of this inventory and challenges that arose with the hope that similar projects will be encouraged in other libraries. Collection analysis was not completed in depth, but general conclusions can be stated about the strengths and weaknesses at this time. Discussion: Because of the length of time since the last inventory was completed, this project took longer than expected. The inventory kit, developed from the lessons learned, will facilitate future inventories at the Dentistry Library, as well as other libraries undertaking a collection inventory. Conclusion: Overall, this was a great learning exercise for the Dentistry Library team, and it resulted in improved access to materials by providing users with the correct item information.
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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.017 |
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".