Prairie soil improves wheat establishment and accelerates the developmental transition to flowering compared to agricultural soils
Bibliographic record
Abstract
Less than 1% of native prairie lands remain in the United States. Located in eastern Washington, the rare habitat called Palouse prairie was largely converted to wheat monocropping. With this conversion came numerous physical, chemical, and biological changes to the soil that may ultimately contribute to reduced wheat yields. Here, we explored how wheat ( Tritcum aestivum L.) seedling establishment, plant size, and heading, signifying the developmental transition to flowering, were affected by being planted in prairie soil versus agricultural soils. We then sought to understand whether the observed effects were the result of changes to the soil microbiota due to agricultural intensification. We found that prairie soil enhanced both the probability of wheat seedling survival and heading compared to agricultural soil; however, wheat growth was largely unaffected by soil source. We did not detect effects on wheat developmental transitions or phenotype when inoculated with prairie microbes compared with agricultural microbes, but we did observe general antagonistic effects of microbes on plant size, regardless of soil source. This work indicates that agricultural intensification has affected soils in a way that changes early seedling establishment and the timing of heading for wheat, but these effects may not be caused by microbes, and instead may be caused by soil nutrient conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".