Fire-induced changes of high and low intensity prescribed fires in a Canadian boreal forest
Bibliographic record
Abstract
The degree of fire-induced effects on boreal forest soils substantially depends on the intensityof fire. Especially high-intensity fires may drastically alter the quality and quantity of the soilorganic matter pool. In this study, we investigated the effects of low and high intensityprescribed fires on soil carbon and nitrogen contents, soil pH, soil temperature, and soilmoisture in a Canadian boreal forest. The study was based on intensive field sampling duringAugust 2018 in Jack pine (Pinus banksiana) forest stands located 50 km north of FortProvidence, Northwest Territories (61.582˚ ; -117.165˚). We measured the soil parametersfrom two short-term fire chronosequences — one with high-intensity prescribed fireshappening in years 2000, 2012, 2015, 2016, and 2017; and the other with low-intensityprescribed fires happening in years 2015, 2017, and 2018. Additionally, we measured soiltemperature and moisture before and after a low-intensity prescribed fire. In thehigh-intensity fire chronosequence, the study site burned in year 2012 had the lowest soiltemperature. Even though temperatures seemed slightly higher in the most recent years ofthe fire chronosequence (2015, 2016, and 2017), we did not identify a clear trend.Soil moisture was the lowest in the study site burned in year 2000, with mostly nosignificant differences between the following years. We did not find significantdifferences in soil moisture and soil temperature before and after a low-intensityprescribed fire. However, both time-after-fire and fire intensity were important forsoil moisture prediction, whereas only fire intensity was important for predictingsoil temperature. Soil pH in the humus layer of the study site burned in 2012 wassignificantly lower compared to the other age classes (no pH data for year 2000) of thehigh-intensity fire chronosequence. Neither C nor N content were significantly differentbetween the fire age classes at the humus layer or at the mineral layers. We believethat the small sample size did not allow the identification of further differencesbetween the age classes, and it prevented direct comparisons between high and lowintensity fires. Despite its exploratory nature, this study offers some insight intoshort-term effects of fire on some soil parameters, for example, the observed changeson soil moisture, soil temperature, and soil pH. Therefore, we will progress thiswork by increasing the sample size and analysing autotrophic and heterotrophic soilrespiration to directly infer on fire-induced changes on the soil organic matter pool.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".