Regeneration dynamics of Great Basin bristlecone pine in southern Nevada
Bibliographic record
Abstract
Great Basin bristlecone pine (Pinus longaeva D.K. Bailey) is an important and long-lived tree species found at high elevations in the interior southwest of the United States, but little is known about its regeneration requirements and response to disturbance. We conducted extensive surveys of seedling regeneration and environmental attributes of regeneration sites in undisturbed forest dominated by this species in the Spring Mountains of southern Nevada. Additional surveys tallied new seedling densities and site attributes 4 years after a wildfire in the same area. Seedlings, saplings, and juvenile trees were less abundant than adult trees in the unburned forest, and soils had lower bulk density and greater depth, moisture, and soil organic matter under adult trees than in open areas. Seedling distributions in both unburned and burned forest showed a negative relationship to a heat load index governed by aspect. The density of new seedlings after the fire was negatively related to distance from unburned forest edges. Seedlings were found in clusters and were associated with adult trees (live or dead) in both unburned and burned stands. Seedling emergence from animal-dispersed caches was more frequent in burned habitats than in unburned habitats. These natural regeneration dynamics provide potential guidance for restoration efforts in this ecosystem.
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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) | 0.001 | 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".