Bibliographic record
Abstract
The historic Cataraqui Cemetery, which was created by an Act of the Legislative Assembly of Upper Canada on August 10, 1850, continues to operate to this day. Over its long history, the cemetery has become the final resting place for over 43,000 individuals. Throughout its history, the Cataraqui Cemetery has managed its charges by a paper-based management system. Through the efforts of a valiant staff, the cemetery has so far managed to keep on top of its affairs. A demonstration computerized information management system was developed for the cemetery. Close work with the staff ensured that the pilot system met their needs. Complicating the process was requirement that the cemetery’s historical method of record keeping be maintained within the demonstration system, as well as dealing with disparaging data sources and types. The demonstration system focused primarily upon managing the cemetery’s rights holders, burial, special provision and maintenance records. All observations of cemetery operations and lessons learned from interviews with the staff were incorporated. The completed system was then demonstrated to the cemetery staff and management in a general meeting. This spurred a debate amongst the staff on the usefulness of such as a system and the need to implement it in the near future.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.977 | 0.960 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".