Effect of Laser Biostimulation on Germination of Sub-Optimally Stored Flaxseeds (Linum usitatissimum)
Bibliographic record
Abstract
Sub-optimal storage of grains could deteriorate seed germination and plant viability. Recent research studies have established that laser biostimulation of seeds could be used as a safe and sustainable alternative to chemical treatment for improving crop germination and growth. Herein, the efficacy of this novel technique is evaluated to see if poor germinability caused by sub-optimal storage of flaxseeds (Linum usitatissimum) could be reversed using laser biostimulation. Healthy flaxseeds were first subjected to sub-optimal storage conditions (30 °C for ten weeks) to degrade their germinability. Two low-cost lasers, including a single-wavelength red laser (659 nm) and a dual-wavelength green/infrared laser (531 and 810 nm (ratio ~10:1)) were then used on two groups viz. healthy (properly stored) and sub-optimally stored (artificially degraded (AD)) seeds and irradiated for 0 (control), 5, 10, and 15 min using total power densities of 7.8 and 6.2 mW/cm2, respectively. In the case of AD seeds, 5-min dual-wavelength laser treatment was found to be the most efficient setting as it improved the mean germination percentage, mean germination time, germination speed, germination rate index, wet weight, and dry weight by 29.3, 16.8, 24.2, 24.2, 15.7, and 20.6%, respectively, with respect to control samples. In the case of healthy seeds, dual-wavelength laser treatment could induce significant enhancement in seeds’ root length, wet weight, and dry weight (improved by 26, 23, and 8%, respectively) under 10 min of irradiation. On the other hand, the effect of applied red laser treatment was not very promising as it could only induce significant enhancement in the mean germination time of AD seeds (improved by 17%). Overall, this study demonstrates the potential of laser biostimulation in reversing the adverse effect of poor crop storage. We believe these findings could spur the development of a physical tool for manipulating seed germination and plant growth.
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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".