Linking juvenile white spruce density, dispersion, stocking, and mortality to future yield
Bibliographic record
Abstract
We examined methods of linking density, dispersion, and stocking information from juvenile regeneration surveys with mortality estimates to predict future yield of white spruce (Picea glauca (Moench) Voss) in boreal mixedwoods. The study focused on data from 709 stands (7–150 years) and defined a stocked plot (10 m2) as having one or more acceptable trees. In juvenile surveys, ingress of natural spruce overwhelmed the regular planting pattern, creating clumped dispersion patterns, as indicated by the Morisita index. A function was developed to describe the relationship between stocking, density, and dispersion. In mature, permanent sample plots, only 30%–40% stocking of 10 m2 plots (700 stems·ha–1) was needed to achieve full yields. Mortality rates for planted spruce varied from 0.1% to 0.8% per year for juvenile stands and from 1.7% to 3.3% per year for mature stands. For rotation-length predictions in Alberta, 0.7% per year is likely a mean mortality loss. These findings were combined to generate stocking versus time curves at a range of mortality rates. The tallest spruce measured in each juvenile survey plot had the same mortality rate regardless of absolute size, and spruce mortality was reduced when associated with aspen. These findings call into question minimum height requirements and free-to-grow criteria in regeneration standards.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".