Bibliographic record
Abstract
b) A surge of 30,000 US troops had been 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. c) Operational changes notwithstanding, US military fatality rates in Afghanistan in PERIOD 11b and throughout PERIOD 12 had remained the same at 6.8 fatalities per 1,000 personnel-years (95 % CI: 6.1 to 7.5; based on 117 deaths in 17,308 pys in each of three 10-week epochs) but decreased to 4 fatalities per 1,000 personnel-years (95 % CI: 3 to 5) in PERIOD 13a. By contrast, UK troops ’ fatality rate decreased from 17 (95 % CI: 11 to 25) and 12 fatalities per 1,000 personnel-years (95 % CI: 8 to 18) in PERIODS 11b and 12a respectively to 4 fatalities per 1,000 personnel-years (95 % CI: 2 to 8) in PERIOD 12b and 3.6 fatalities per 1,000 personnel-years (95 % CI: 1.5 to 7.5) in PERIOD 13a; with the Canadian decrease having occurred even earlier- 15 fatalities per 1,000 personnel-years (95 % CI: 6 to 29) in PERIOD 11b versus 2 fatalities per 1,000 personnel-years (95 % CI: 0.4 to 5.4) during the 30 weeks of
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.656 | 0.496 |
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".