Post-harvest regeneration of montane Abies amabilis forests in northern Washington, USA
Bibliographic record
Abstract
The effects of regeneration treatments on current stand composition were analyzed in a high-elevation forest approximately 20 years after harvest in the Cascade Range, Mt. Baker-Snoqualmie National Forest, Washington (USA). Post-harvest treatments included sites that were (1) broadcast burned and planted with Abies amabilis, (2) unburned and seeded with A. amabilis and A. procera, and (3) unburned and planted with A. amabilis. All sites are currently dominated by A. amabilis and Tsuga heterophylla. Burned-planted sites have a smaller proportion of A. amabilis than unburned sites, and burned sites have less advance regeneration of all species than unburned sites. Although future stand composition is difficult to predict, comparison with historical stand data for this location indicates that regeneration treatments, including seeding and planting, have not had significant effects on overstory species dominance. If rapid regeneration of A. amabilis is a management objective, then post-harvest burning should be avoided to encourage advance regeneration of this species. Key words: Abies amabilis, forest regeneration, subalpine forest
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".