Fire deficit around Canadian boreal communities - source data
Bibliographic record
Abstract
These are the source files used in Parisien et al. (2020) “Fire deficit increases wildfire risk for many communities in the Canadian boreal forest”. These files will allow the user to recreate the results of the study. - The study area boundary was defined by a selection of the Ecological zones of Canada (Ecological_zones_studyarea.zip). - Fire-regime zones are from Erni et al. (2020) (FRT_Canada_studyarea.zip). - Vegetation cover is based on the National FBP fuels grid version (2014b) (Beaudoin et al. 2014) (National_FBP_Fueltypes_version2014b.zip). - Community points are based on Natural Resources Canada 2010 Populated Places dataset, hand-corrected using aerial imagery (Boreal_communities_160.zip). - Fire polygons are based on the National Burned Area Composite 'NBAC' for 2002-2017 (NBAC_2002_2017.zip) and the National Fire Database 'NFDB' for 1978-2001 (NFDB_1978_2001.zip). - Fire ignition points are based on the National Fire Database 'NFDB' for 1988-2017 (NFDB_ignitions_1988_2017.zip). - Harvest sensitivity analysis is based on Canada Landsat Disturbance (CanLAD) dataset (Guindon et al., 2018) (CanLaD_2011cutblocks.zip). - Land cover sensitivity analysis is based on 2019 National Risk Analysis Fuels Map (Natoinal_Risk_Fuels_2019.zip) and 2011 MODIS Land Cover map (MODIS_Landcover_2011.zip). Statistics calculated in the paper include: percentage of recently burned forest (RBF) for 1978-2007, 1988-2017, and 1998-2017; ignition density for human- and lightning-caused fires; and percentage of area harvested 1985-2015 (Guindon et al., 2018). Datasets are periodically updated an may vary from the version provided here. Versions available here were last accessed: Oct. 2018 (NBAC, Bing Maps), June 7, 2019 (NFDB, 2014 Fuels, 2011 MODIS Land Cover), Dec. 2018 (2019 Fuels), and Nov. 2018 (Ecozones, Fire Regime Zones, and Community shapefiles). Works cited here: Beaudoin, A., P. Y. Bernier, L. Guindon, P. Villemaire, X. J. Guo, G. Stinson, T. Bergeron, S. Magnussen, and R. J. Hall. 2014. Mapping attributes of Canada’s forests at moderate resolution through kNN and MODIS imagery. Canadian Journal of Forest Research 44:521-532. Erni, S., Wang, X., Taylor, S., Boulanger, Y., Swystun, T., Flannigan, M., & Parisien, M. A. (2020). Developing a two-level fire regime zonation system for Canada. Canadian Journal of Forest Research, 50(999), 259-273. Guindon, L., Bernier, P., Gauthier, S., Stinson, G., Villemaire, P., & Beaudoin, A. (2018). Missing forest cover gains in boreal forests explained. Ecosphere, 9(1), e02094.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".