Bibliographic record
Abstract
Beginning in the Late 1990s? 2. What Effects Could the September 11, 2001 Terrorist Attacks on the United States and the U.S. Military Response to Them Have on Military Compensation and Benefits?3. What Kinds of Increases in Military Pay and Benefits Have Been Considered or Used in the Past? 4. How Are Each Year's Increases in Military Pay Computed? 5. What Have Been the Annual Percentage Increases in Active Duty Military Basic Pay Since 1993 (FY1994)?What Were Each Year's Major Executive and Legislative Branch Proposals and Actions on the Annual Percentage Increase in Military Basic Pay? 6.Is There a "Pay Gap" Between Military and Civilian Pay, So That Generally Military Pay Is Less than That of Comparable Civilians?If So, What Is the Extent of the "Gap"?7. What Major Changes to Active Duty Military Pay and Benefits Have Been Made in Recent Years in Addition to Any Changes in Each Year's Overall Percentage Increase in Basic Pay? 8. What Are the Major Military Pay-Related Items in the Current (FY2002) National Defense Authorization Act as Passed by Congress, Other than Percentage Increases in Basic Pay?
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.318 | 0.250 |
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".