Effect of Laser Biostimulation on Germination of Wheat
Bibliographic record
Abstract
Highlights The effect of laser biostimulation on wheat germination was explored using a single- and dual-wavelength (DW) laser. An 80 mW green-infrared DW laser treatment significantly enhanced several germination traits of wheat. The effect of a 100 mW single-wavelength red laser on wheat germination was non-significant. Abstract. Laser biostimulation of seeds has established itself as a safe and sustainable alternative to genetic modification or chemical use to enhance plant germination and growth. A knowledge gap, however, exists to define optimal laser parameters for different seeds as inappropriate irradiation may seriously damage or destroy germinability. To this end, the effect of laser biostimulation on germination of Canada Western Red Winter (CWRW) wheat seeds was evaluated using two low power portable lasers: 1) a single-wavelength red laser (659 nm) and 2) a dual-wavelength (DW) green/infrared laser [531 and 810 nm (ratio ~10:1)]. The seeds were pretreated with laser light before germination tests for 5, 10, and 15 minutes using total power densities of ~14.2 and 11.3 mW/cm2, respectively. Laser treatment with a DW laser for a duration of 10 min was found to be the most efficient as it significantly enhanced mean germination time, germination rate index, germination speed, number of roots, and hypocotyl length by 14.3%, 15.2%, 15.2%, 31.8%, and 60.9%, respectively, with respect to control samples. The effect of single-wavelength red laser on CWRW wheat seed germination traits was not statistically significant. To the best of our knowledge, this study is the first on evaluating the effect of DW laser treatments in plant biostimulation and introduces a new pathway for manipulating the germination, growth, and development of seeds/plants. Keywords: Agriculture, Biostimulation, Dual-wavelength laser, Laser, Wheat.
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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".