Recent Crown Thinning in a Boreal Black Spruce Forest Does Not Reduce Spread Rate nor Total Fuel Consumption: Results from an Experimental Crown Fire in Alberta, Canada
Bibliographic record
Abstract
A 3.6 ha experimental fire was conducted in a black spruce peatland forest that had undergone thinning the year prior. After 50 m of spread in a natural stand at 35–60 m min−1, the crown fire (43,000 kW m−1 intensity using Byram’s method) encountered the 50% stem removal treatment; spread rates in the treatment were 50–60 m min−1. Fuel consumption in the control (2.75 kg m−2) was comparable to the treatment (2.35 kg m−2). Proxy measurements of fire intensity using in-stand heat flux sensors as well as photogrammetric flame heights had detected intensity reductions to 30–40% of the control. Crown fuel load reductions (compensated by higher surface fuel load) appear to be the most significant contributor to the decline in intensity, despite drier surface fuels in the treatment. The burn depth of 5 cm in moss and organic soil did not differ between control and treatment. These observations point to the limited effectiveness (likely reductions in crown fire intensity but not spread rate) of stem removal in boreal black spruce fuel types with high stem density, low crown base height and high surface fuel load. The observed fire behaviour impacts differ from drier conifer forests across North America.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".