Bibliographic record
Abstract
... Fire Weather tells the story of the 2016 Fort McMurray wildfire in Alberta, Canada. The fire burned approximately 600,000 hectares of boreal forest, swept through the oil and mining city of Fort McMurray, forced the evacuation of 88,000 people, and destroyed more than 3000 houses. However, the book goes far beyond this fire; it is essentially a reflection on the Petrocene—that is, the petroleum age defined by Vaillant as “the period of history (about the past 150 years) in which our pursuit of fire's energy, most notably crude oil, in conjunction with the internal combustion engine, transformed all aspects of our civilization and, with it, our atmosphere.” In other words, the Petrocene is the period in which we filled the air with carbon dioxide, methane, and other greenhouse gases. Although the Fort McMurray fire may not have been the most severe in the world history, it serves as an illustrative and shocking example of the consequences of the Petrocene, including mainly climate change but also the widespread use of petroleum-derived products (plastics and laminates) in our homes. In that sense, the book delves into Pyne's pyric transition between fire and combustion (Pyne 2021). Through the book, Vaillant draws a parallel between the wildfire, which started by burning forest fuels, and the destruction of Fort McMurray—a city whose existence is rooted in extraction of fossil fuels. That is, a city living from fossil fuels (extinct), created a few millions of years ago, was destroyed by recent fuels (extant). Furthermore, the burning of these fossil fuels is the driver of the extreme fire weather conditions that allowed the wildfire to escalate into ferocious firestorm that destroyed the city. Vaillant makes it clear that these firestorms are no surprise; scientists, by the 1960s, had already predicted that drastic changes in climate and fire behavior would occur if we continued releasing tons of carbon dioxide into the atmosphere. However, many individuals, companies, and governments deliberately ignore (and discredit) those warnings to prioritize their economic gains from the oil industry. This behavior is having a profoundly negative impact on our lives today and, even more so, on future generations—a masterful example of the tragedy of the commons.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.010 |
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".