Creating a Comprehensive Western American/Canadian Fire Dataset, 1880-2018
Bibliographic record
Abstract
The currently available fire-history data of Western North America (US/Canada) available for geographic and other analyses is largely piecemeal and difficult to find. Data from before the 1980s is scattered among many sources and held by a plethora of different agencies. The aim of this project was to change that daunting reality and provide a single dataset that would fill that data gap and make doing research on and mapping of fires in the late 19th and early 20th centuries more accessible. This data encompasses 138 years (1880 - 2018), 12 US states, three Canadian provinces and two Canadian territories. Currently existing digital datasets were combined and streamlined into a single dataset with unified attributes and units. During this process, I found a total of 143,702 fires that totaled 1,793,542.62 sq. kilometers of burned area. The final data is available to the public via the web as a set of ESRI shapefiles.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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 teacher head, 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".