Seed pre-treatment using a derivative of 5-hydroxybenzimidazole (AMBIOL) pre-acclimates carrot seedlings to drought
Bibliographic record
Abstract
The possibility that AMBIOL, a derivative of 5-hydroxybenzimidazole, may promote drought acclimation of carrot seedlings was studied. Carrot seeds were pre-treated by soaking in AMBIOL solutions of 0, 0.1, 1 or 10 mg L–1 for 24 h and germinated. Fifteen-day-old seedlings were exposed to a 7-d drought by withholding water until soil moisture content declined to one-third of the initial values. Drought caused a significant reduction in xylem pressure potential, elongation growth, leaf area expansion, root growth and dry matter production. Membrane capacitance, net photosynthesis, stomatal conductance, transpiration and water use efficiency all declined in all plants under drought. However, seed pre-treatment using AMBIOL 0.1 and 10 mg L–1 completely alleviated the drought-induced reduction in shoot dry matter production. Seed preconditioning using AMBIOL 10 mg L–1 promoted dry matter production, which was 214% higher than in the untreated droughted plants and was even 26% higher than that of the untreated, unstressed controls. AMBIOL appeared to have induced drought acclimation through root adjustments that enhanced root growth, possibly supplying root-derived essential factors. Key words: Acclimation, antioxidant, carrot, drought, dry matter production, 5-hydroxybenzimidazole, growth, membrane capacitance
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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) | 0.002 | 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".