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Record W2805876038 · doi:10.1177/1548512918771769

Estimating Ricochet Hazard Zones at Sea

2018· article· en· W2805876038 on OpenAlexafffund
Ramzi Mirshak, Talia Beech

Bibliographic record

VenueThe Journal of Defense Modeling and Simulation Applications Methodology Technology · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsDefence Research and Development Canada
FundersMinistère de la Défense NationaleDefence Research and Development Canada
KeywordsProjectileHazardComputer scienceGeologySimulationPhysics

Abstract

fetched live from OpenAlex

Naval operators commonly report that when projectiles impact the ocean surface, they ricochet mainly to the right but that some rounds ricochet in wildly unpredictable directions. This observation, which leads to considerable uncertainty with regard to the resulting hazard zone for projectiles fired at sea, seems contradictory to observations from controlled experiments where projectiles with similar incident angles tend to ricochet in a more predictable manner. In this paper, we postulate that the likely cause of the discrepancy is ocean waves. Past work examining the effect of waves on ricochet is extended to model the risk area related to projectile ricochet at sea. Ricochet results from controlled experiments are incorporated into a simple model that combines a two-degree-of-freedom ballistic model and a series of analytically derived wave fields with different amplitudes and directions of travel. For the purposes of demonstration in this paper, data for different .50 calibre projectiles are used to populate the model. The results support the notion that waves have a considerable effect on ricochet hazard zones.

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: Simulation or modeling · Consensus signal: Simulation or modeling
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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.071
GPT teacher head0.324
Teacher spread0.252 · 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 designSimulation or modeling
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

Citations1
Published2018
Admission routes2
Has abstractyes

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