A Simple, Semi-Automated, Gravimetric Method to Simulate Drought Stress on Plants
Bibliographic record
Abstract
Drought is a major constraint of global crop production. Given that drought-induced crop losses can threaten world food security, it has been and continues to be the focus of a large body of interdisciplinary research. Most drought experiments are conducted under controlled environmental conditions, where maintaining accurate soil moisture content is critical. In this study, we developed a simple, Arduino microcontroller-based, semi-automated, lysimeter that uses the gravimetric method to adjust soil moisture content in pot experiments. This method employs an Arduino microcontroller interfaced with a balance as part of a portable lysimeter and irrigation system which can weigh and record the mass of plants growing in pots, determine water loss due to evapotranspiration, and adjust soil moisture automatically to a desired relative soil water content. The system was validated with a greenhouse pot experiment using a panel of 50 early-maturity Canadian soybean varieties. Drought was induced in the experiment by adjusting soil moisture content to 30% field capacity while maintaining control pots at 80%. Throughout the experiment, the two moisture levels were efficiently maintained using the Arduino-based lysimeter. Plant physiological responses confirmed that plants in the drought treatment were under physiological stress. This semi-automated lysimeter is low-cost, portable, and easy to handle, which allows for high-throughput screening.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".