Seed tree density, variable retention, and stand composition influence recruitment of white spruce in boreal mixedwood forests
Bibliographic record
Abstract
In an operational-scale experiment, we examined natural regeneration of white spruce ( Picea glauca (Moench) Voss) under various degrees of variable retention harvesting across different overstory canopy compositions. We sampled four types of overstory canopy compositions (ranging from deciduous dominated to conifer dominated) and six rates of retention (2%, 10%, 20%, 50%, 75% and 100%). Spruce seedling densities, maximum height, and stocking rates were assessed in 432 transects and were found to be lowest in the deciduous stands and with 100% canopy retention (control). Regression tree analysis indicated that densities, maximum height, and stocking of spruce seedlings were greatest with higher availability of seed trees (>30·ha–1) and on machine corridors; in these circumstances stocking reached 74%. By contrast, stocking was less than 14% on retention strips with no machine traffic, when seed tree density was less than 11 seed trees·ha–1. However, stocking also declined with higher density of residual trees. Stocking in relation to density of seed trees was highest within clearcuts and lowest within the 75% retention intensity where low light transmission may have limited regeneration success. Passive soil disturbance from skidding appeared to be more important for the establishment of spruce seedlings than was the abundance of competing vegetation.
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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 | 0.001 | 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 | 0.000 | 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".