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Record W2299219723 · doi:10.1119/1.4944359

Indoor Microgravity Survey

2016· article· en· W2299219723 on OpenAlexafffund
Richard A. Secco, Reynold E. Sukara

Bibliographic record

VenueThe Physics Teacher · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicExperimental and Theoretical Physics Studies
Canadian institutionsWestern University
FundersOntario Research Foundation
KeywordsGravitational accelerationMeasure (data warehouse)AccelerationStopwatchPhysicsGravitational fieldPendulumSimulationGeodesyComputer scienceClassical mechanicsGeology

Abstract

fetched live from OpenAlex

There are many lab exercises for upper-level school students and freshman undergraduates to measure the value of the local acceleration due to gravity (g) near Earth's surface. In these exercises, the value of g is usually taken to be constant. The approach is often based on measuring the period of a pendulum that is inversely proportional to the square root of g. Traditional measurements of the period of a simple or inclined pendulum involve use of a stopwatch to measure the time required to complete a number of oscillations, but other more sophisticated measurement techniques for greater accuracy, such as a photogate timing system, measuring the time-dependent tension on the string, or using a stepper motor connected to a conical pendulum have been described. Using video imaging, the mechanics of objects dropped from some height has also been used to determine g. In physics courses where physical principles are applied to Earth problems, however, the goal is usually to measure a change in a potential field, such as Earth's gravitational field, in order to determine anomalous subsurface characteristics. In this paper, we describe an indoor exercise to measure the local change in g resulting from a large anomalous mass near the observation location.

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)0.000
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 score0.011
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.004

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 head0.019
GPT teacher head0.264
Teacher spread0.245 · 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

Citations6
Published2016
Admission routes2
Has abstractyes

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