Development of Norway spruce dominated stands after single-tree selection and low thinning
Bibliographic record
Abstract
The study included 23 stands (at least 2 ha each in size) distributed from southern to northern Finland. These Norway spruce (Picea abies (L.) Karst.) dominated stands grew on fertile (OxalisMyrtillus and Myrtillus site types) mineral soils. Each stand contained two substands randomly treated with single-tree selection or low thinning. The harvested volumes (trees > 9 cm) varied greatly but averaged 94 m3·ha1 in the former consisting of mainly medium-sized and larger (>15 cm) trees and 68 m3·ha1 in the latter of mainly medium-sized and smaller (<15 cm) trees. After treatment, mean diameter at breast height (DBH), basal area, and stand volume were 1217% lower in single-tree selection than in low thinning. The stem distributions were reverse-J shaped and bell shaped, respectively. During the monitoring of a mean of 11 years, about one cutting cycle in single-tree selection, stand volume (trees > 9 cm) increased 38% in single-tree selection and 27% in low thinning. The respective current annual volume and relative increments were 5.4 (3.6%) and 4.6 m3·ha1·year1 (2.4%). In 18 (volume) and in 22 (relative) of the 23 plot pairs the increment was higher after single-tree selection than after low thinning (p values 0.013 and <0.001, respectively). Single-tree selection plots additionally included 1300 saplings/ha (from breast height to DBH 9 cm) after cutting, with the transition of 80 saplings/ha into larger trees and with the ingrowth, mainly spruce, of 170 seedlings/ha into saplings during monitoring.
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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) | 0.000 | 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".