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Record W2134255174 · doi:10.5539/apr.v2n2p192

Empirical Formula Prediction on Critical Impact Energy for Scabbing Phenomena on Concrete Structures

2010· article· en· W2134255174 on OpenAlex
Ismail Abdul Rahman, Ahmad Mujahid Ahmad Zaidi, Qadir Bux alias Imran Latif, Muhammad Yusof Ismail

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueApplied Physics Research · 2010
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsMissileComputer scienceKinetic energyComputer securityAerospace engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Concrete is basic construction material used for numerous sort of structure. However, in the mainstream crucial structures have to be designed as self-protective such as nuclear plants, Power plants, Weapon Industries, weapons storage places, water retaining structures like dams, & etc., which provides protection against any tragedy incident or intentionally produced horrible incidents such as dynamic loading, incident occurs in nuclear plants, terrorist attack, war, missile attack, and etc. This paper questioningly is paying concentration on judgment on minimum required kinetic energy for scabbing on the concrete structures generated by flat nosed hard missile using curve fitting empirical study. Argue overcome from this newly developed empirical formula can be used for making design recommendations and design procedures for determining the dynamic reaction of the target to frustrate scabbing.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.100
GPT teacher head0.443
Teacher spread0.343 · 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