Bibliographic record
Abstract
Abstract: Tree invasions into meadows near upper timberline reduce diversity of habitat and diminish high-elevation food sources for black (Ursus americanus) and grizzly (Ursus arctos) bears. How serious is this threat in view of future climate change? Tree invasions observed in the Pacific Northwest o New Mexico suggest that climatic restraints to forest expansion have relaxed since the end of the Little Ice Age. Because climate patterns are large-scale phenomena, geographic synchronicity in tree establishment might be expected if a warming trend began. When tree invasion chronologies from Canada to New Mexico were compared, 2 synchronicities of climate and tree invasions appeared, indicating a possible climatic influence. However, forest retreat and meadow advance are also commonly observed at high elevations. The mechanism of retreat is usually fire followed by slow or unsuccessful regeneration of forest. There is no clear evidence based upon tree seedling chronologies that meadows will continue to be lost on the basis of climate change alone. Climate warming may set the stage for forest advance, but tree invasions are highly sensitive to local conditions. Concentrated grazing by domestic or wild animals in high-elevation meadows may trigger tree invasion by reducing competition to tree seedlings from established meadow vegetation. Prescribed fires or natural fires allowed to burn within prescriptions can be used as a tool for maintaining meadows and bear habitat under some of the projected climate change scenarios for western North America.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.078 | 0.018 |
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".