The exposure of prairie soils to millennia of climate variability and change
Bibliographic record
Abstract
The native grassland ecosystems of the Northern Great Plains (NGPs) are adapted to millennia of climate variability. Field experiments have examined the response of native prairie to a range of historical weather conditions that are unrepresentative of the full spectrum of hydroclimatic variability since grasslands became established on the NGP. We review existing paleo-environmental records of Holocene vegetation and climate and present a tree-ring reconstruction of the hydroclimate of the past millennium. Sedimentation rates and pollen in cores from prairie lakes reveal significant disturbance of native prairie under drought conditions, but also relatively rapid recovery of the grassland when the climate cycles back to wetter conditions. Tree-ring signals of prolonged and severe drought coincide with the reactivation of sand dune fields and indicators of reduced vegetation cover and soil erosion in the lake sediments. Climate model projections of future changes in the soil water balance suggest that prairie grasslands will be subject to drier conditions like those in the paleoclimate record from the mid-Holocene and more recent medieval warm period. The stability and resistance of prairie soils to wind and water erosion, as a function of the resilience of native grassland, contrast with the agroecosystems that have replaced most of the native prairie. By casting a unique paleo-environmental perspective on the resilience of native prairie as a protective soil cover, this paper provides a context and further support for agricultural practices that restore and maintain soil health.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".