Evidence of different compounds in smoke derived from legumes and grasses acting on seed germination and seedling emergence
Bibliographic record
Abstract
Abstract Our previous study showed that smoke derived from alfalfa ( Medicago sativa ) caused different germination responses compared with that from prairie hay ( Festuca hallii ) and wheat straw ( Triticum aestivum ), but the mechanism remained unclear. In this study, we used Salad Bowl lettuce ( Lactuca sativa ) as a quick bioassay to trace the active compounds in each of these three smoke solutions. Column chromatography and high performance liquid chromatography (HPLC) were used to separate and identify active fractions. Seeds of four species from Fescue Prairie were primed for 24 h at room temperature in darkness using serial dilutions of separated active fractions, as well as karrikinolide (KAR 1 ). After priming, seeds were dried at room temperature in darkness for 7 days and subsequently incubated at 10/0°C or 25/15°C in 12 h light–12 h dark or 24 h darkness for 49 days. KAR 1 was in the smoke made from prairie hay, and wheat straw, but was absent in alfalfa smoke. Priming in KAR 1 solutions increased germination of three native species. Priming in highly concentrated KAR 1 reduced radicle length of Cirsium arvense , the only non-native species. Even though KAR 1 has the potential to enhance regeneration of native species in the Fescue Prairie, KAR 1 is not universally present in smoke derived from different plant materials. Unknown compound(s) in smoke derived from legumes remain to be identified.
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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 | 0.000 | 0.000 |
| 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".