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The LongPen™—The World’s First Original Remote Signing Device*

2010· article· en· W2023478387 on OpenAlexaffabout
Diane M. Kruger

Bibliographic record

VenueJournal of Forensic Sciences · 2010
Typearticle
Languageen
FieldComputer Science
TopicHandwritten Text Recognition Techniques
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsHandwritingInscribed figureComputer scienceVisual artsHistoryComputer graphics (images)ArtArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

The LongPen is a remote-controlled pen and videoconferencing device conceived by Canadian author Margaret Atwood in 2004 and initially intended to bring "live" author signings to far away locations. The LongPen allows for individually inscribed long distance signatures and writing while maintaining an original record, written with pen and ink. LongPen specimens were compared with control specimens using different speeds, pen pressures, and types of pens. Preliminary indications are that LongPen inscriptions can be identified or associated with their author. Size and form are maintained and artifacts are subtle. Some limitations with respect to the capture of long tapered strokes, delicate connecting strokes, and differences in line width were noted. Factors which may impact forensic handwriting examinations include limited amounts of writing, light pen pressure, date of the writing, type of writing instrument, dimensions of the writing, and failure to consider that the device has been used.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.005

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 head0.024
GPT teacher head0.296
Teacher spread0.272 · 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 designBench or experimental
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
Published2010
Admission routes2
Has abstractyes

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