Bibliographic record
Abstract
On January 12, 2010, a catastrophic earthquake measuring 7.3 on the Richter scale struck Haiti close to Port-au-Prince. The primary quake and its aftershocks caused widespread devastation, and killed or injured hundreds of thousands of people.The Government of Canada launched its humanitarian response, dubbed Operation HESTIA, on January 13. Within hours of the first tremor, a team of military and civilian emergency management experts deployed to Haiti, quickly followed by the Canadian Forces Disaster Assistance Response Team (DART) and a group of urban rescue specialists. Operation HESTIA is one of the quickest humanitarian responses Canada has ever made to a crisis in another nation.Joint Task Force Haiti was the Canadian Forces humanitarian operations task force deployed under Operation HESTIA. Totalling more than 2,000 personnel, it included land, maritime and air components working together under the command of Brigadier-General Guy Laroche.As well as the DART, the land component included the 3rd Batallion Royal 22e Régiment Battalion Group, with two rifle companies, a support company and a squadron of engineers from 5 Combat Engineer Regiment; 1 Canadian Field Hospital, with the personnel and equipment to mount a 100-bed medical facility; and the Joint Task Force Support Element to provide all the logistics services required to deploy the task force, support its operations, and bring it back home to Canada.The maritime component consisted of the destroyer HMCS Athabaskan and her CH-124 Sea King helicopter detachment, and the frigate HMCS Halifax.The air component was made up of airfield operations personnel and a flight of six Griffon helicopters. Flying the air bridge to Canada were strategic and tactical airlift provided by Canadian Operational Support Command.As well as Joint Task Force Haiti, Operation HESTIA involved a multi-disciplinary civilian team: diplomats and consular officials from the Department of Foreign Affairs and International Trade Canada (DFAIT), development experts from the Canadian International Development Agency (CIDA), immigration personnel from Citizenship and Immigration Canada (CIC), and security experts from the Canadian Border Security Agency (CBSA), the Royal Canadian Mounted Police (RCMP) and Public Safety Canada (PSC).The entire whole-of-government humanitarian effort, including both military and civilian assets, was coordinated under the leadership of Ambassador Gilles Rivard, Canada’s head of mission in Haiti.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.150 | 0.022 |
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".