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Record W3088297006 · doi:10.1119/10.0002063

Smartphones and Gravitational Acceleration I: Overview

2020· article· en· W3088297006 on OpenAlexaff
Meryem Berrada, Joshua A. H. Littleton, Richard A. Secco

Bibliographic record

VenueThe Physics Teacher · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicExperimental and Theoretical Physics Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsEquatorGravitational accelerationPhysicsAccelerationGravitationRADIUSLatitudeCentrifugal forceBulgeEarth's rotationRotation (mathematics)GeodesyMechanicsClassical mechanicsAstronomyGeologyStarsGeometryMathematicsRotational speed

Abstract

fetched live from OpenAlex

Geodesy is a very active and essential research discipline in geophysics but it is not a commonly studied subject at the secondary school or junior post-secondary levels. Far more frequently, gravity and gravitational acceleration are discussed, to some extent, in elementary kinematics or classical mechanics courses. This often takes the form of the force acting on a body or bodies due to gravity, or that the acceleration (agrav) of a free-falling body is 9.8(1) m/s2—which implies the setting of the question is at Earth’s surface. While the latter is a reasonable and practical approximation, agrav observed over the surface of Earth varies and is dependent on several factors. These are normally related to elevation and latitude variations caused by Earth’s rotation. Earth’s rotation contributes negatively to agrav at the equator due to the centrifugal force outward and equatorial bulge, which makes the equatorial radius larger than the polar radius. Offsetting this partially is the positive contribution to agrav at the equator caused by the equatorial bulge because of the extra mass comprising the bulge.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0160.006

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 head0.044
GPT teacher head0.280
Teacher spread0.236 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations8
Published2020
Admission routes1
Has abstractyes

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