Post-fire Recovery of Soil Organic Layer Carbon in Canadian Boreal Forests, 2015-2018
Bibliographic record
Abstract
This record is for the dataset “Post-fire Recovery of Soil Organic Layer Carbon in Canadian Boreal Forests, 2015-2018” at https://doi.org/10.3334/ORNLDAAC/2235. This dataset provides site moisture, soil organic layer thickness, soil organic carbon, nonvascular plant functional group, stand dominance, ecozone, time-after-fire, jack pine proportion, and deciduous proportion for 511 forested plots spanning ~140,000 km2 across two ecozones of the Northwest Territories, Canada (NWT). The plots were established during 2015-2018 across 41 wildfire scars and unburned areas (no burn history prior to 1965), with 317 plots in the Plains and 194 plots in the Shield regions. At each plot, two adjacent 30-m transects were established 2 m apart, running north from the plot origin. Soil organic layer (SOL) depth (cm) was measured every 3 m and the mean was taken from the 10 measurements to calculate a plot-level SOL thickness. Three soil organic layer profiles were destructively sampled at 0, 12, and 24 m using a corer that was custom designed for NWT soils. Within the transects, all stems taller than 1.37 m were identified to species to calculate tree density (stems / m2). Nonvascular plant percent cover was identified to functional group at five, 1-m2 quadrats spaced 6 m apart along the belt transect. A subset of 2,067 of 5,137 total increments from 1,803 profiles from 421 plots were analyzed for total percent C using a CHN analyzer. Time-after-fire was established using fire history records. For older plots where no known fire history is recorded, tree age was used. Data are for the period 2015-06-11 to 2018-08-24 and are provided in comma-separated values (CSV) format. This dataset can be downloaded at: https://doi.org/10.3334/ORNLDAAC/2235.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".