Recent military fatalities in Afghanistan by cause and nationality:
Bibliographic record
Abstract
Note: A surge of 30,000 US troops was deployed to Afghanistan to facilitate Operation Moshtarak, which began in 2010. By PERIOD 11b, US deployment of 90,000 by province was reckoned as 20,000 to Helmand, NK2 to Kandahar and NK3 elsewhere. In June 2011, towards the end of PERIOD 14a, US President Obama announced the start of draw-down of US troops – an initial 5,000 to 10,000 in 2011. Canadian troops will not have a combat role after 2011. In 2012, UK troops are reckoned to have reduced to 9,500. Withdrawal of French troops will begin in July 2012 and be completed by the end of 2012. Summary We begin with a calendar-year resume which relies only on total number of UK military fatalities in each calendar year and a mid-year estimate for the number of UK troops deployed to Afghanistan. Our resume lacks detail on when troop numbers escalated and does not differentiate, as our more detailed analyses do, between the ‘fighting season ’ and Afghan winter. Nonetheless, the resume convey some key features: i) 2011 is the first of the past six calendar years in Afghanistan when UK troops have faced less than major combat (which we define operationally as: 6 fatalities per 1,000 personnel-years) and ii) 2009 and 2010 exacted a very heavy toll indeed. Calendar year UK military fatalities in Afghanistan, F Mid-year UK troop deployment, P Estimated UK fatality-rates per 1,000 personnel-years, based on F and P only
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".