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Record W2934656036 · doi:10.26434/chemrxiv.7701695.v1

Click, Zoom, Explore: Interactive 3D (i-3D) Figures in Standard Manuscript PDFs

2019· preprint· en· W2934656036 on OpenAlexaff
Sourav Chatterjee, Sooyeon Moon, Amanda Rowlands, Fred Chin, Peter H. Seeberger, Nabyl Merbouh, Kerry Gilmore

Bibliographic record

VenueChemRxiv · 2019
Typepreprint
Languageen
FieldArts and Humanities
TopicAcademic Writing and Publishing
Canadian institutionsSimon Fraser University
FundersDeutsche Forschungsgemeinschaft
KeywordsZoomComputer scienceSoftwareVariety (cybernetics)Reading (process)AdobeBeautyComputer graphics (images)MultimediaArtArtificial intelligenceAestheticsProgramming languageEngineeringLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

While chemistry exists in three-dimensions, it is published in two. This down-conversion results in a significant loss in information and often necessitates multiple images/figures to convey the complexity, intricacy, and beauty of a given structure. Outlined herein is a concise, straightforward method for incorporating interactive three-dimensional (i-3D) figures into manuscript pdfs. These figures can be generated from a variety of sources and allow for structures, molecular orbitals, unit cells and crystal lattices, as well as biopolymers to be published in the same information rich format as they are created and studied on our computers. These images can be seen and interacted with by anyone reading the manuscript in the standard pdf software (Adobe Reader) – and to fully appreciate this article, please read it using Adobe Reader. It is time for chemistry publications to take advantage of the digital age.

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)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.707

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.5040.306

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 head0.069
GPT teacher head0.276
Teacher spread0.206 · 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.

Study designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2019
Admission routes1
Has abstractyes

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