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Record W2796917461 · doi:10.1093/jogss/ogx028

The Effectiveness of Rocket Attacks and Defenses in Israel

2017· article· en· W2796917461 on OpenAlexaff
Michael J. Armstrong

Bibliographic record

VenueJournal of Global Security Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicNuclear Issues and Defense
Canadian institutionsBrock University
Fundersnot available
KeywordsRocket (weapon)PillarAeronauticsSoftware deploymentEngineeringEnhanced Data Rates for GSM EvolutionComputer securityAerospace engineeringComputer scienceStructural engineeringTelecommunications

Abstract

fetched live from OpenAlex

This empirical article studies rocket attacks and defenses in Israel during operations Protective Edge, Pillar of Defense, and Cast Lead, and also during the Second Lebanon War. It analyzes publicly available counts of rockets fired, fatalities, casualties, and property damage. The estimates suggest that interceptor deployment and civil defense improvements both reduced Israel's losses slightly during Pillar of Defense and substantially during Protective Edge. They also imply that interceptor performance during Pillar of Defense may have been overstated. Ground offensives were the most expensive way to prevent rocket casualties. Interceptors were at least as cost-effective as military offensives, and their advantage improved over time. Without its countermeasures, Israel's rocket casualties could have been more than fifty times higher during Operation Protective Edge. These results imply that Israel's rocket concerns were more justified than critics admit, but its military operations were less worthwhile than intended.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.031
GPT teacher head0.395
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2017
Admission routes1
Has abstractyes

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