Postfire seedling recruitment at the southern limit of lichen woodland
Bibliographic record
Abstract
Although fire is the primary mechanism driving regeneration in open black spruce ( Picea mariana (Mill.) BSP) lichen woodland, there are limited data concerning the sources of seedling mortality across the range of burn severity. We monitored planted seedlings in areas of high and low burn severity in Terra Nova National Park (Newfoundland, Canada) to determine sources and patterns of mortality of black spruce seedlings among burn treatments following a recent burn (2002). The importance of herbivory by small mammals as a source of seedling mortality was evaluated using small cages that excluded voles and non-native snowshoe hare. Overall seedling mortality was high (79%) in all areas; mortality was similar in areas of low (73%) and high (76%) burn severity, and highest in edge areas adjacent to closed-canopy forest (90%). Drought, rather than herbivory, was the most common cause of mortality during the first two seasons following germination. Seedling mortality at the southern edge of the lichen woodland was comparable to that found in other studies, but sources differed, emphasizing the spatially variable nature of mortality. Based on the level of seedling recruitment, our results suggest lichen woodland will return at this site.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".