Bibliographic record
Abstract
The organization had its headquarters in Ottawa. The headquarters scientific staff was organized as closely as possible along the lines of the staff in one of the Armed Services so that the headquarters scientific staff could work closely at all levels with the Services. It was through this staff that the Board got from the Services detailed knowledge of their requirements: if, was not’ just a formulated statement af requirements but also-what they were thinking about or worrying about and it was also through the contact of the two staffs that the Board got back not only the results of its research but, also the results of any other scientific research which was relevant, and was available either in Canada or from other countries. Headquarters scientific staff was responsible after it got the requirements from the services for trying to place these requirements in suitable research agencies where a solution may be found. Attempts were made to use all the resources that were available in Canada and these resources could be roughly divided into four qroups: first of all. The laboratories run by the Defence Research Board itself ; second, a group of Government laboratories mostly federal, but some provincial also ; thirdly, the universities and finally the industrial research laboratories
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.014 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.115 | 0.018 |
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".