Bibliographic record
Abstract
ImagIne a lIne of flames several mIles long roaring swiftly up mountains and down valleys, incinerating an area more than five times the size of the District of Columbia.The fiery wave leaves behind a quiet landscape of charred brush, trees and horizons.Peggy hernandez (Nrm '82), the newly appointed supervisor of the los Padres national forest, surveys the landscape once blackened by the Zaca fire, the second largest in California's recorded history.fire season is now officially over.January rains have saturated the burn area more than five months after the inferno was contained.most of the 240,000 affected acres were in the los Padres national forest."This fire was a devastating event, but in some ways a blessing since this is such a remote area," said Hernandez as she eyes a once-blackened hillside.fire can be a natural management tool for cleaning out dead vegetation and debris.for a typical wildfire, the main challenge is re covery -the landscape needs time to heal.overseeing fire recovery is just one of many supervisor duties for Hernandez, a California native and first-generation college graduate.she is responsible for the operations of the entire 1.76-million acre los Padres national forest, extending approximately 220 miles from Big sur to the ventura/los angeles county line.she oversees a staff of nearly 500 and a $20-million budget.continued on next page… the smoldering aftermath of the Zaca Fire, the second largest in california's history.(Photo by ray Ford)
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.759 | 0.585 |
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".