Revealing the Process: An Infrared Photographic Analysis of the Work of Paul Kane
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".