A 249-year chronosequence of forest plots from eight successive fires in the eastern Canada boreal mixedwoods
Bibliographic record
Abstract
A combination of wildfires and defoliating insect outbreaks play an important role in the natural successional dynamics of North American boreal forests, which, in the long term, change the post-disturbance composition and structure of forest stands. After stand-replacing disturbances (mainly wildfires), early successional hardwoods typically dominate the affected areas in boreal forests. Provided sufficient time following disturbances, the increasing recruitment of mid- to late-successional softwoods as well as the mortality of hardwoods gradually change forest composition from hardwoods to admixtures of hardwood-conifer species and conifer-dominated stands in mid and late successional stages, respectively. Such mixed woods are abundant across the southern Canadian boreal forest. In boreal Canada, mixed woods are the most structurally heterogeneous forest ecosystems, are highly productive, and form an important source of timber supply. Here we present the EASTERN BOREAL MIXEDWOODS CANADA dataset, which documents the changes in composition and structure of stands originating from eight successive wildfires representing a chronosequence of 249 years in eastern Canada. This dataset has been used in several different projects to study and model the influence of natural (e.g., insect outbreaks) and anthropogenic disturbances (e.g., harvesting) on the dynamics of post-fire stands. The data covers a high range of variability in stand composition and structure, explained by species establishment, dominance and mixture. It thus constitutes a useful source of information to trace the dynamics of the main boreal tree species of eastern north America, from their establishment to their replacement at different spatial scales (e.g., from stand to landscape level).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".