Examining abiotic and biotic factors influencing specimen black oaks (Quercus kelloggii) in northern California to reimplement traditional ecological knowledge and promote ecosystem resilience post-wildfire
Bibliographic record
Abstract
California black oak, Quercus kelloggii, plays an important role in the lifeways of many Indigenous tribes throughout California. Native peoples tend black oaks using traditional ecological knowledge (TEK) to encourage the development and proliferation of specimen oaks. These mature, large, full crowned trees provide a disproportionate amount of ecosystem services, including acorns and habitat, when compared to smaller black oaks. Altered approaches to land management and the cessation of frequent low intensity cultural burns places these specimen oaks at risk from encroachment, forest densification, and severe wildfire. This project is a collaboration between academic researchers and a Native Advisory Council to examine abiotic and biotic factors influencing Quercus kelloggii to reimplement traditional ecological knowledge and promote ecosystem resilience post-wildfire. Data were collected from 55 specimen black oaks at Pepperwood Preserve in northeast Sonoma County. Specifically, we classified specimen oak growth habitat by measuring specimen oak crown area and live crown ratio, the size and number of surrounding trees, and amount of surface and ladder fuel loads. The preserve burned in both the Tubbs Fire in 2017 and the Kincade Fire in 2019, the latter of which occurred three months after the completion of the initial data collection. Immediately following the Kincade Fire we measured scorch height and related our abiotic and biotic variables to fire severity. Forest densification was found to have a significant negative effect on both canopy area (p = 0.003) and live crown ratio (p = 0.038) of the specimen oaks. Densification did not affect surface and ladder fuel load accumulation since the Tubbs Fire in October 2017 (p > 0.05). Neither surface and ladder fuels nor forest densification variables significantly affected scorch height on the specimen oaks (p > 0.05) following the Kincade Fire.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".