Recent trends in post-wildfire seeding in western US forests: costs and seed mixes
Bibliographic record
Abstract
Broadcast seeding is one of the most commonly used post-fire rehabilitation treatments to establish ground cover for erosion control and mitigation of non-native plant species invasions. Little quantitative information is available on overall trends of post-fire seeding expenditures and seed mixes used over time in forested ecosystems in the western USA. We reviewed scientific articles, government publications and unpublished documents as well as USDA Forest Service Burned Area Reports to determine trends in post-fire seeding in forested ecosystems over time. Of 1164 USDA Forest Service Burned Area Reports, 380 contained information on seeding treatments conducted in forested ecosystems. A review of 40 papers and 67 Burned Area Reports reporting species seeded between 1970 and 2007 revealed a trend of increasing use of native species, annual cereal grains and sterile-grass hybrids, with natives dominating seed mixes. According to 380 Burned Area Reports with data on costs and area seeded, total post-fire seeding expenditures have increased substantially, averaging US$3.3 million per year spent on post-fire emergency seeding treatments in forested ecosystems that involved the Forest Service during the period 2000 to 2007 – an increase of 192% compared with the average spent during the previous 30 years. The percentage of the total burned area seeded averaged 21% in the 1970s, compared with only 4% between 2000 and 2007.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".