Bibliographic record
Abstract
Hardwood-dominated temperate forests (mostly in Eastern North America, Europe, North East Asia) provide valuable renewable timber and numerous ecosystem services. Many of these forests have been subjected to harvesting or conversion to agriculture, sometimes over centuries, that have greatly reduced their former extent and diversity. Natural regeneration following harvesting or during post-agricultural succession has often failed to restore these forests adequately. Past harvesting practices and the valuable timber of some species have led to a reduction in their abundance. The loss of apex predators has caused herbivore populations to increase and exert intense browsing pressure on hardwood regeneration, often preventing it. Particularly important are fruit, nut and acorn bearing species, because of their vital role in forest food webs and biodiversity. Restoring hardwood species to natural forests in which they were formerly more abundant will require a number of forest management actions (e.g., resistant hybrids, deer exclosures/protectors, enrichment planting, underplanting, etc.). Similarly, reforesting areas that were once natural forests will also require new silvicultural knowledge. Global warming trends will intensify the need for interventions to maintain the diversity and function of temperate hardwood forests, as well as for increase hardwood reforestation.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.017 |
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".