Bibliographic record
Abstract
Library and Archives Canada (LAC) has made significant changes in the storage of the institution's cellulose nitrate collection and made great progress towards solving the problem of inaccessibility concerning nitrate panoramic film.LAC's vast collection of nitrate material includes 600,000 photographic negatives of which approximately 4,000 panoramas have remained inaccessible to both the public and archivists. The panoramas had previously been stored in their original housings, often multiple negatives per metal canister, at an offsite storage facility that was not considered conducive for the long-term preservation of nitrate material.As part of the preparations to move the nitrate collection to LAC's new state-of-the-art Nitrate Film Preservation Facility (NFPF) staff described, assessed, measured, and rehoused each panoramic negative by separating the panoramas, individually rolling, then wrapping them in bond paper and placing them in boxes in an upright position. In February 2011, the entire nitrate collection was relocated to this new facility, which includes a collection processing room and a digitization room.Building on the initial research undertaken by Greg Hill and Tania Passafiume (presented at the IS&T conference in 2006[1]), this paper will outline how staff established a successful workflow to digitize a selection of 1,500 panoramas from the Merrilees collection. This collection is in high demand but the panoramas have been restricted in access due to the fragility of the material. The selected panoramas are mainly images of Canadian Expeditionary Force battalions from the First World War, with the negatives measuring up to 2.6 metres (9 feet) in length. After digitization the images will be uploaded to the LAC website. In light of the upcoming 100th anniversary of the start of the First World War it is anticipated that the public, through web-based crowdsourcing, will help in the identification of many of the soldiers in the panoramas.This discussion will highlight several challenges such as the limited options of equipment and setup available when digitizing nitrate panoramic film, and dealing with a medium affected by deterioration. A complete review will also be presented of the new process developed to obtain a high resolution preservation scan thereby allowing public access to the panoramas while simultaneously preserving the original material in a secure and controlled environment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".