Influence of precommercial thinning on understory vegetation of young-growth Sitka spruce forests in southeastern Alaska
Bibliographic record
Abstract
The effects of precommercial thinning on the understory vegetative cover of 16- to 18-year-old spruce–hemlock ( Picea sitchensis (Bong.) Carrière – Tsuga heterophylla (Raf.) Sarg.) stands were studied in seven replicate areas over seven growing seasons postthinning. Vegetative cover was analyzed at the class level, but species-specific effects were examined in relation to their value as food for Sitka black-tailed deer ( Odocoileus hemionus sitkensis Cowan). When compared with unthinned controls, thinned stands (3.6–6.3 m spacing) had significantly greater understory cover. However, all thinned spacings led to similar understory cover. Conifer cover recovered to about two-thirds of its prethinning level within seven growing seasons posttreatment. Understory nonconiferous cover increased during the first 2–4 years postthinning but began to decline with increasing conifer cover during the next 3 years, nearly reaching pretreatment levels by year 7. In unthinned understories, vegetative cover had declined and was significantly lower than that beneath thinned stands. Summer food resource values for deer were increased by thinning. Winter food resource values were increased by thinning for snow-free conditions but were unaffected for conditions when herb-layer forbs were buried by snow.
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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".