Reclaiming the Prairie: Natural Resource Management at Prairie State Park
Bibliographic record
Abstract
Once widespread from central Canada to Mexico, tallgrass prairie is now one of North America's most imperiled ecosystems. Historically, tallgrass prairie covered roughly 15 million acres of Missouri. Today, less than 60,000 acres remain, comprising small patches dispersed across the state. The largest of these is Prairie State Park, a 1,619 Hectare piece of tallgrass prairie in Missouri. Perhaps the most pressing issue affecting prairie ecosystems in the Great Plains is woody plant encroachment, including species such as eastern red cedar (Juniperus virginiana), sericea lespedeza (Lespedeza cuneata), sumac (Rhus spp.), and blackberry (Rubus spp.). Eastern red cedar is particularly detrimental to native prairies because it outcompetes herbaceous species by depriving them of water, sunlight, and nutrients. Pittsburg State University students recently completed an internship where they helped revive a degraded section of Prairie State Park that was overgrown with eastern red cedar. Now in the second year of this partnership, park staff mentored the students in a variety of practical skills, including mechanical vegetation control, prescribed fire, all-terrain vehicle (ATV) use, personal protective equipment (PPE) protocols, and resource management strategies. Thanks to the combined efforts of park staff and PSU students, the degraded prairie section has been largely restored.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".