MétaCan
Menu
Back to cohort
Record W2741879055 · doi:10.1021/acs.jchemed.7b00314

Hands-On Data Analysis: Using 3D Printing To Visualize Reaction Progress Surfaces

2017· article· en· W2741879055 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Chemical Education · 2017
Typearticle
Languageen
FieldChemistry
TopicVarious Chemistry Research Topics
Canadian institutionsUniversity of British Columbia
FundersUniversity of OttawaNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaMerck
Keywords3D printingComputer scienceSet (abstract data type)3d printedNanotechnologyComputer graphics (images)Data scienceMultimediaMaterials scienceEngineeringMechanical engineeringManufacturing engineeringProgramming language

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Advances in 3D printing technology over the past decade have led to its expansion into all subfields of science, including chemistry. This technology provides useful teaching tools that facilitate communication of difficult chemical concepts to students and researchers. Presented here is the use of 3D printing technology to create tangible models of reaction progress surfaces. Easy-to-follow step-by-step instructions are provided for the creation of these surfaces from IR, NMR, and HPLC data. More generally, this procedure enables conversion of any arrayed data set into a 3D-printable STL file. The general utility of these 3D-printed models is highlighted with three unique case studies.

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)0.002
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 score0.028
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.096
GPT teacher head0.451
Teacher spread0.355 · 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