Alternative Technologies to Replace Antipersonnel Landmines
Bibliographic record
Abstract
Executive Summary Introduction Definition History of Mines Residual Hazards of Mines International Instruments The U.S. Position Committee Process Report Road Map National Security Environments and the Context National Security Strategies Benefits and Vulnerabilities of New Technologies Current Uses of Antipersonnel Landmines Doctrinal Guidance for Using Landmines Role of Landmines in Warfare Capabilities of Antipersonnel Landmines Technologies in Antipersonnel Landmiens Evaluation Methodology Methodology Baseline Systems Criteria Alternatives Available Today Overview Nonmateriel Alternatives Materiel Alternatives Committee Assessments Alternatives Available by 2006 Overview Nonmateriel Alternatives Materiel Alternatives Committee Assessments Alternatives Potentially Available After 2006 Overview Materiel Alternatives Committee Assessments Conclusions and Recommendations Introduction Alternatives Available by 2006 Alternatives Potentially Available After 2006 Self-Destructing, Self-Deactivating Fuzes References Appendixes A Biographical Sketches of Committee Members B Committee Meetings C Current Types of U.S. Landmines D Value of Antipersonnel Landmines in Unprotected Mixed Minefields E The Ottawa Convention and Amended Protecol II of the Convention on Conventional Weapons F Signatories to the Ottawa Convention and their Alternatives to Landmines G Mission Need Statements
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.012 |
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".