An alternative to clear-cutting in the boreal forest of Alaska: a 27-year study of regeneration after shelterwood harvesting
Bibliographic record
Abstract
We present 27-year results from a comparison of clear-cutting and shelterwood harvesting in the boreal forest of Alaska. Three patch clear-cut and three shelterwood units were harvested in 1972; about 100 dispersed white spruce (Picea glauca (Moench) Voss) leave trees per hectare were retained in the shelterwoods. Units were mechanically scarified and an exceptionally large seed-crop was dispersed that year. Shelterwood trees were removed after 15 years. After 27 years, overstory treatment had no effect on the density or growth of the species we studied, while scarification had highly significant effects. In 1999, scarified areas were densely populated with white spruce seedlings and saplings (118 000 129 000 stems/ha, with spruce in 100% of plots). Unscarified areas had far fewer spruce stems but were nevertheless well stocked (11 000 15 000 stems/ha, with 87% frequency). Initially, spruce grew best on scarified surfaces, but by 27 years, growth of the tallest spruce saplings was significantly greater on unscarified than scarified surfaces. By 27 years, cover of the grass Calamagrostis canadensis (Michx.) Nutt. had returned to preharvest levels in all treatment types. Because criteria for evaluating forest management practices have changed since this study was begun, partial overstory retention systems for the management of Alaska's boreal forest deserve further study.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".