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Record W4241362381 · doi:10.24908/iqurcp.9201

Revealing the Process: An Infrared Photographic Analysis of the Work of Paul Kane

2018· article· en· W4241362381 on OpenAlexvenueaboutno aff
Ian E. Longo

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicConservation Techniques and Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStudioPhotographyPaintingVisual artsOil paintingArtArt historyHistory

Abstract

fetched live from OpenAlex

The ability to examine a painter’s working methods is not only valuable to art historians and conservators
 but, in the case of the early Canadian painter, Paul Kane (1810-1871), provides crucial information as to the accuracy and historical value of many of the scenes he painted. Paul Kane’s approximately 130 works in oil, painted in his Toronto studio from sketches made during his two voyages through the Canadian northwest during the 1840s, have told us much about the culture and lifestyle of the Métis, Sioux and other native Canadian peoples before the advent of photography. The historical accuracy of these paintings has now been called into question by the use of low-cost infrared photography. By utilizing infrared photography in the 700-1100nm range, we are now able to see what lies beneath the surface layers of oil paint and see the many changes Kane made between his observations in the field and his final product in the studio. Changes range from the slight adjustment of a headdress to the creation of entirely fictional features in the landscape. In some cases changes were made to conform to the expectations of his patrons. Although infrared photography has already been used in the world of art history for some time, recent advances in modifying consumer-level DSLR cameras, the development of advanced quartz-fluoride optics, and novel imaging processing techniques have transformed Infrared photography into an affordable and efficient way to study Paul Kane’s artistic process.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.004
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 head0.156
GPT teacher head0.376
Teacher spread0.220 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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
Published2018
Admission routes2
Has abstractyes

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