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Record W4240608265 · doi:10.32920/ryerson.14656908

Caring For Panoramic Photographs In Small Collections

2021· preprint· en· W4240608265 on OpenAlexaff
Kathleen A. Kinakin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDigitizationFlatteningCollections managementComputer graphics (images)Visual artsComputer scienceArtEngineeringWorld Wide WebComputer vision

Abstract

fetched live from OpenAlex

Silver gelatin panoramic photographs produced in the first half of the 20th century are commonly found in archives and collections. Many were rolled for ease of storage, and their present condition means the information in the images is inaccessible. Panoramic prints in collections, whether rolled or flat, present a challenge to archivists and collection managers in terms of their treatment, housing, digitization and storage. Working with rolled panoramic prints from a private collection as a case study, this thesis examines possible solutions to the issues of caring for panoramas in collections. Conservators and collection managers were also consulted to broaden the survey. A procedure for digitizing rolled panoramas is proposed as a solution for providing access to the image information if conservation treatment is not a viable option. Humidification and flattening treatments are discussed, and solutions are presented for the housing, storage and digitization of these oversized objects.

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)0.003
metaresearch head score (Gemma)0.011
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0300.007

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 head0.045
GPT teacher head0.232
Teacher spread0.187 · 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
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

Citations0
Published2021
Admission routes1
Has abstractyes

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