Postdispersal seed predation of white spruce in cutblocks in the boreal mixedwoods: a short-term experimental study
Bibliographic record
Abstract
We conducted a 1-year study to examine the influence of postdispersal seed predation by small mammals and other vertebrate predators on seedling recruitment rates (percentage of seeds surviving to seedling stage) and seed loss of white spruce (Picea glauca (Moench) Voss) in recently logged areas in the boreal mixedwood forest in north-central Alberta. Experimental exclosures showed that predators reduced recruitment rates, on average, by 46% for seeds exposed to predation for 6 weeks in the summer and by 79% for seeds exposed to predation between autumn (seed dispersal) and the following summer (germination). We were unable to detect an influence of initial seed density on recruitment rates. A seed tray experiment suggested that predators can detect and consume 80%94% of white spruce seeds available in cutblocks within 1 month of sowing and that seed predation rates do not vary predictably with distance from the cutblockforest edge. Our results suggest that vertebrate seed predators, especially small mammals, have the potential to severely affect recruitment rates of white spruce in recent cutblocks, although long-term work is needed to understand how predators might affect recruitment under natural conditions with predator and seed densities that vary in space and time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".