When the flame goes out: an exploration of landscape change using repeat photography related to Indigenous burning in Kananaskis Country, Alberta
Bibliographic record
Abstract
Fire exclusion has defined 20th century forestry practices in North America and produced many unintended consequences. In the Canadian Rocky Mountains, the removal of fire from the landscape caused significant landscape changes over the past century. Mountain forests are now more uniform in stand composition and structure, and understorey diversity is reduced. These changes mean that forests are now more susceptible to high-intensity, difficult-to-control wildfires. Re-introducing Indigenous led historical burning patterns modeled on traditional burning techniques can be a restoration technique for these highly altered ecosystems. Indigenous fire regimes that emphasized regular, low-intensity burning created forests that had less fuel build up and were not as susceptible to dangerous wildfires. In order to effectively re-introduce historical fire regimes onto the Canadian mountain landscapes, it is essential to understand the history of human management of landscapes with fire. This project uses new methods of oblique image analysis that build on recent developments in oblique image analysis to examine the historical management of a portion of the traditional territory of the Stoney Nakoda Nation that overlaps present day Kananaskis Country in Alberta, Canada. While it is difficult to capture low-intensity Indigenous burns using traditional fire reconstruction methods, in-depth analysis of historical photos taken before the introduction of fire suppression laws may reveal new insights into historical fire regimes. Images were classified using machine learning software and compared to images classified by a human to verify the accuracy of the machine learning software. A case study of georeferencing images was also conducted, with the landcover estimates generated by georeferenced images compared to oblique estimates. Spatial signatures of Indigenous burning were identified and applied to repeat image sets to look for visual evidence of Indigenous burning on the landscape. The results from this study provide a useful starting point for further research into repeat photography and Indigenous burning.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".