Plant Diversity and Cover after Wildfire on Anthropogenically Disturbed and Undisturbed Sites in Subarctic Upland <i>Picea mariana</i> Forest
Bibliographic record
Abstract
Postfire development of cover and diversity was studied in an upland Picea mariana-dominated forest in the Canadian Subarctic. Short-term vegetation responses of 10- and 22-year-old cleared rights-of-way and a forest site were investigated two and three growing seasons after a wildfire. Prefire and postfire investigation of the study site allowed direct comparison of species cover and frequency values, as well as the Shannon-Wiener diversity index, before and after the fire. The fire considerably reduced diversity on all sites. Species diversity increased with the level of prefire disturbance. Prefire disturbance influenced the fire's characteristics by altering the fuel load and soil moisture, which in turn affected the postfire revegetation through different soil and microclimatic conditions. The sites that were most severely disturbed before the fire experienced the most rapid revegetation, including the highest diversity index and highest plant cover. Of the sites that were undisturbed before the fire, the natural drainage swales offered the best growing conditions after the burn. Furthermore, prefire disturbance increased the patchiness of the burned area, and the residual flora of unburned patches added to postfire floristic diversity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".