Military fatality rates (by cause) in Afghanistan and Iraq: a measure of hostilities
Bibliographic record
Abstract
BACKGROUND: Military fatalities occur in clusters, and causes differ between theatres of operation or within-theatre over time. AIM: Based on around 500 coalition deaths, identify major causes in Iraq and Afghanistan. For consecutive periods (1: May 1 to September 17, 2006, 2: September 18, 2006 to February 4, 2007), ascertain UK and others' numbers deployed to compare fatality rates per 1000-personnel years. Take account of clustering: deaths per fatal improvised explosive device (IED) incident, and in making short-term projections for Afghanistan. METHODS: Cause and date of coalition deaths in Iraq and Afghanistan are as listed in http://www.iCasualties.org, where each death is designated as hostile or non-hostile. Numbers deployed in 2006 were available for UK and Canada, and for US to Iraq. FINDINGS: Out of 537 coalition fatalities in Iraq in 2006 to September 17, 2006, 457 (85%) were hostile, but only half were in Afghanistan (October 2001 to September 17, 2006: 52%, 249/478). Air losses accounted for 5% fatalities in Iraq, but 32% in Afghanistan. IEDs claimed three out of five hostile deaths in Iraq, only a quarter in Afghanistan. Deaths per fatal IED incident averaged 1.5. In period 1, 50/117 military deaths in Afghanistan were UK or Canadian from 6750 personnel, a fatality rate of 19/1000/year, nearly four times the US rate of 5/1000/year in Iraq (based on 280 deaths). Sixty out of 117 fatalities in Afghanistan occurred as clusters of two or more deaths. In period 2, fatality rates changed: down by two-thirds in Afghanistan for UK and Canadian forces to 6/1000/year (18 deaths), up by 46% for US troops in Iraq to 7.5/1000/year (416 deaths). INTERPRETATION: Rate, and cause, of military fatalities are capable of abrupt change, as happened in Iraq (rate) and Afghanistan (rate and cause) between consecutive 140-day periods. Forecasts can be wide of the mark.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".