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Record W4306640188 · doi:10.1515/cti-2022-0009

A billion times smaller than us: helping students comprehend the molecular scale

2022· article· en· W4306640188 on OpenAlexafffund
Jaspreet Sidhu, J. Scott McIndoe

Bibliographic record

VenueChemistry Teacher International · 2022
Typearticle
Languageen
FieldChemistry
TopicVarious Chemistry Research Topics
Canadian institutionsUniversity of Victoria
FundersUniversity of Victoria
KeywordsAvogadro constantScale (ratio)Mobile deviceGlobeVisualizationNanotechnologyPhysicsMaterials scienceComputer scienceData miningQuantum mechanicsPsychologyWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract Comprehension of molecular scale is an essential component of a chemistry student’s education. However, it is especially difficult for most to wrap their heads around just how small the nanometer scale is at which the molecules they are taught about exist. Using 3D printing techniques to aid in visualization, we can model spherical molecules, namely buckminsterfullerene (C60) and the cuboctahedral gold cluster Au55, and scale them up by eight orders of magnitude. The new size of these molecules is comparable to a globe 13 cm in diameter, a model of the Earth scaled down by eight orders of magnitude. Seeing and holding both of these objects resized to similar dimensions, students are able to get a sense of how the molecular scale compares to the handheld scale. The fact that the molecule is scaled up by a factor of 1024 in volume also nicely contextualizes the magnitude of Avogadro’s number (∼0.6 × 1024), the constant of proportionality that converts the molecular scale to the handheld scale.

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)0.002
metaresearch head score (Gemma)0.007
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0050.006
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0190.009

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 head0.018
GPT teacher head0.290
Teacher spread0.271 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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