Exploring the potential of two-aged white spruce plantations for the production of sawlog volume with simulations using SORTIE-ND
Bibliographic record
Abstract
The main objective for even-aged plantation (EAP) management of producing sawlog material has driven practices towards low initial planting densities and lower post thinning densities. For semi-shade tolerant species, the resulting stand density potentially leaves enough growing space for the introduction of a second cohort of trees in the understory, making it a two-aged plantation (TAP). TAPs could have many silvicultural benefits, especially in sensitive areas where intensive treatments associated with EAPs are incompatible with local management objectives. White spruce (Picea glauca) is a good candidate species for modeling TAPs because it is the most widely planted tree species in Canada and has proven tolerance to understory planting. SORTIE-ND, a single-tree spatially explicit growth model was used to explore the yield of variable density and rotation length scenarios when each white spruce cohort is introduced mid rotation, compared to traditional even-aged management. All TAP scenarios tested produced more sawlog volume and more merchantable volume than equivalent densities of EAPs. The lowest density tested, 400 stems ha-1 planted every 35 years, had the highest sawlog yields (3.23 m3 ha-1 yr-1). Considering smaller size products changes the optimum TAP scenario but maintains the advantage over EAPs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".