Impact of seed and seedling predation by small rodents on early regeneration establishment of black spruce
Bibliographic record
Abstract
Black spruce (Picea mariana (Mill.) BSP) postdispersal seed and juvenile seedling predation by small rodents (Peromyscus maniculatus (Wagner), Clethrionomys gapperi (Vigor), and Phenacomys intermedius (Merriam)) was assessed in three boreal habitats over a 2-year period using an extensive exclosurecontrol experiment. Small rodent relative abundance was measured during six periods using snap trapping. We found that seed and juvenile seedling predation by small rodents varied according to habitat type and over time. Indeed, seed predation was higher in sprucemoss forests than in other habitats, notably during the winter of 2002. During this period, seedling predation was higher in recent burns. This period of higher seed and juvenile seedling predation corresponded to an increase in small rodent abundance in our study area. We suggest that seeds and juvenile seedlings can become important food sources for small rodents during winter when fresh and succulent vegetation is rare, as shown by results of seed predation. The impact of small rodents on the early regeneration of black spruce in the eastern Canadian boreal forest is thus an important factor to consider to better understand the forest regeneration process in this particular biome. Rodents can have a major effect on regeneration following a burn and can also contribute to poor seedling establishment from natural seed rain under mature cover.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".