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Record W2972494605 · doi:10.69554/obyb9764

Efficient appraisal and processing of disk images of legacy digital storage media at the Canadian Centre for Architecture

2018· article· en· W2972494605 on OpenAlexaboutno aff
Tim Walsh

Bibliographic record

VenueJournal of digital media management · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsArchitectureComputer scienceDigital storageComputer graphics (images)Computer architectureOperating systemComputer hardwareArtVisual arts

Abstract

fetched live from OpenAlex

The Canadian Centre for Architecture (CCA) is an international research institution based in Montreal that produces exhibitions, publications and a range of activities driven by a curiosity about how architecture shapes – and might reshape – contemporary life. As a collecting institution interested in contemporary architecture, the CCA also preserves, manages and provides access to architectural archives, including those in digital formats. This paper describes the development of the Disk Image Processor, a set of Python scripts and corresponding graphical user interface developed at the CCA for processing disk images of legacy digital storage media in the CCA collection, such as 3.5- and 5.25-in floppy disks, Zip disks, optical media and hard drives. By streamlining the processes of appraisal, characterisation, file export and submission information packaging, the CCA has been able to scale its digital archiving activities to match its research programmes and acquisitions.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.006

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 head0.012
GPT teacher head0.220
Teacher spread0.207 · 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
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

Citations1
Published2018
Admission routes1
Has abstractyes

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