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Record W2050346489 · doi:10.1139/p11-084

On high-altitude projectile motion

2011· article· en· W2050346489 on OpenAlexvenueno aff
Ján Beňačka

Bibliographic record

VenueCanadian Journal of Physics · 2011
Typearticle
Languageen
FieldEngineering
TopicSports Dynamics and Biomechanics
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsProjectileRange of a projectileDragGravitational accelerationSupersonic speedAltitude (triangle)AccelerationTrajectory of a projectileMechanicsRange (aeronautics)Drag coefficientClassical mechanicsProjectile motionComputational physicsGeodesyGravitationGeometryAerospace engineeringGeology

Abstract

fetched live from OpenAlex

In this study, the formulas for projectile velocity components and coordinates in a vacuum were derived with the altitude decrease in gravitational acceleration factored in. A model of cannon shell motion in the air is presented that accounts for the altitude dependence of gravitational acceleration, air density, the speed of sound up to an altitude of 84 km, and the speed dependence of the drag coefficient at trans- and supersonic speeds. The drag coefficient function is obtained by fitting to experimental data taken for the US M101 155 mm shell. The model gives flight parameters that agree with the published ones. The motion of the Paris Gun projectile is then modeled. The model shows that a range of 120 km is possible if the projectile mass is about 150 kg. A flat Earth approximation was used in the computations. Changing the launch angle to 90°, super high-altitude vertical ascent and free fall are modeled.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.167
Teacher spread0.155 · 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

Citations7
Published2011
Admission routes1
Has abstractyes

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