Bibliographic record
Abstract
The Chemical, Biological, Radiological-Nuclear, and Explosives (CBRNE) Research and Technology Initiative (CRTI) is part of Canada's response, helping to sharpen the focus of our scientific and emergency response communities on the areas that are the most relevant to today's realities. Many previous and ongoing CRTI projects have greatly strengthened Canada's preparedness for CBRNE events. However; there are additional needs, such as the need to increase the preparedness for dealing with vulnerable population groups, the need for identifying gaps in casualty management and the need for increasing capacity in emergency response. The Workshop on Biological Dosimetry: Increasing Capacity for Emergency Response was held on 19 May 2010, followed by the Workshop on Medical Preparedness for CBRNE Events: National Scan on 20 to 21 May 2010 and the Workshop on Radiological Emergency Preparedness for Children on 1 to 2 June 2010. All three workshops were hosted by CRTI in Ottawa, Canada. The purpose behind these workshops was to enhance communications and networks within the emergency response community, identify the needs and gaps in emergency preparedness and response in CBRNE events and eventually generate a plan for the development of emergency casualty management capabilities with specific emphasis on various population groups.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.431 | 0.254 |
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".