Differential effects of acorn burial and litter cover on<i>Quercus rubra</i>recruitment at the limit of its range in eastern North America
Bibliographic record
Abstract
Primary predators or dispersers such as birds and rodents cache acorns of northern red oak (Quercus rubra L.). A proportion of these acorns are not retrieved, and thus, animals may favour oak regeneration by placing acorns in microsites suitable for recruitment. We experimentally investigated the effects of acorn burial and litter cover on red oak recruitment at two sites at the northern limit of the species' range in North America. Laboratory experiments also tested the effects of acorn burial and litter cover on desiccation and germinability and the influence of soil moisture on germination. Burial and litter protected acorns against predation by deer in the field. Germination was promoted by burial both in field and laboratory experiments. Germination was proportional to acorn water content and to soil moisture. Seedling emergence in the field was enhanced by burial but reduced by litter cover. Acorns buried but uncovered by litter had the highest probability of recruiting a seedling. A potential effect of seed predators or dispersers on red oak regeneration and expansion is suggested, as acorn caching by birds and rodents may actually enhance population recruitment, despite high mortality through acorn consumption.Key words: acorn burial, litter, microsite effects, recruitment, northern red oak, southern Quebec.
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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".