Priming seeds in aqueous smoke solutions to improve seed germination and biomass production of perennial forage species
Bibliographic record
Abstract
Seeds of many forage species have dormancy, delaying germination in the field. Species from semi-arid environments may have adaptation to fire, therefore, the effects of priming seeds of common forage species in aqueous smoke solutions were studied. Seeds of eight grasses and two legumes were primed in serial dilutions of aqueous smoke solutions and then dried before germination tests. Depending on the species and concentrations of solutions, priming seeds acted independently or in interaction with light and (or) temperature to improve germination in Altai wildrye (Leymus angustus), green needlegrass (Nassella viridula), Kura clover (Trifolium ambiguum), needle-and-thread (Hesperostipa comata), northern wheatgrass (Elymus lanceolatus), orchard grass (Dactylis glomerata), plains rough fescue (Festuca hallii), Russian wildrye (Psathyrostachys juncea), and western wheatgrass (Pascopyrum smithii). Priming seeds of cicer milkvetch (Astragalus cicer) in distilled water or aqueous smoke solutions had no effect on germination (P > 0.05). Priming seeds in aqueous smoke solutions partially substituted the light requirement for germination in seven species. Priming seeds in aqueous smoke solutions before sowing in the field did not change total seedling emergence (P > 0.05), but increased standing crop for orchard grass and Plains rough fescue. Overall, the beneficial effects of priming in smoke solutions on perennial age species are less than species from environments adapted to more frequent fires.
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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".