Smoke originating from different plants has various effects on germination and seedling growth of species in Fescue Prairie
Bibliographic record
Abstract
Little is known about how smoke, an important germination cue, influences seed regeneration of species in Fescue Prairies. Whether germination and seedling growth responses vary with smoke produced from different materials is still ambiguous. In this study, seeds of four forbs from a Fescue Prairie were primed in serial dilutions of aqueous smoke solutions produced from alfalfa (Medicago sativa L.), prairie hay (Festuca hallii (Vasey) Piper), and wheat straw (Triticum aestivum L.), and incubated at 10–0 °C or 25–15 °C in a 12 h light – 12 h dark cycle or 24 h darkness for 49 d. Nonprimed seeds and those primed in distilled water were used as controls. Germination and radical length of Conyza canadensis (L.) Cronquist increased after priming in concentrated smoke-solutions derived from alfalfa, but decreased after priming in the same concentrated smoke solutions made from prairie hay and wheat straw at 25–15 °C in 24 h darkness. Smoke substituted for light improved germination of Artemisia ludoviciana Nutt. Our results indicate that the effect of smoke on seed germination and seedling growth was temperature- and light-dependent. It appears that smoke produced from alfalfa had different compounds that, in turn, had different germination and seedling growth responses as compared with the smoke produced from prairie hay and wheat straw.
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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 | 0.001 |
| 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".