Data For: The Influence of Post-Fire Recovery and Environmental Conditions on Boreal Vegetation
Bibliographic record
Abstract
This data was gathered to explore patterns of vegetation recovery after fire in the boreal forests of the Northwest Territories, Canada. Vegetation and environmental data was collected from a chronosequence of sampling sites ranging from one to 275 years after fire covering both the Taiga Shield and Taiga Plains Ecoregions. The dataset is comprised of three data sheets: Jorgensen et al_SpeciesRelativeAbundance includes presence and absence data per plot for a selection of vascular plant and lichen species, as well as associated environmental variables and site information. Each species has a "presence" column and an "absence" column, adding to five, indicating the number of quadrats per plot where the species was found. Jorgensen et al_VegetationCommunityComposition includes abundance indices (from 1-5) per plot for all vascular and non-vascular species used in manuscript ordinations (excluded: species present in only one plot, species causing anomalies - see manuscript for more information). Jorgensen et al_SeedlingSaplingCounts includes counts of the number of seedlings and saplings <1.3m tall per sampling plot for black spruce (Picea mariana), paper birch (Betula papyrifera), and trembling aspen (Populus tremuloides).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".