Arbuscular mycorrhizae alleviate water deficit in <i>Dipteryx alata</i> Vogel: seedling quality and resilience
Bibliographic record
Abstract
Dipteryx alata Vogel (Fabaceae) is a fruit tree species native to the Cerrado with ecological and economic potential. However, a water deficit can be a limiting factor to the initial growth of this species, requiring knowledge on technologies that can alleviate this stressful effect. We hypothesized that inoculation with arbuscular mycorrhizae fungi contributes to stress mitigation during and after water deficit. Dipteryx alata seedlings were subjected to two water regimes (control: seedlings irrigated daily; and water deficit: irrigation suspension), associated with inoculation with arbuscular mycorrhizal fungi (AMF) (AM–, without inoculation; AM+, inoculation with Rhizophagus clarum), and three evaluation periods (T0, time zero; F0, zero photosynthesis (7 days of water restriction); REC, recovery (100 days)). Water deficit impaired water relations, decreasing the quality of D. alata seedlings. AM+ seedlings showed higher relative water content (RWC), leaf area ratio, chlorophyll index, and Rubisco carboxylation capacity (A/Ci), which helped in photosynthetic metabolism. Inoculation with R. clarum alleviated the impact of stress on water use efficiency, water potential, RWC, and A/Ci in REC. Inoculation with AMF is a promising management technique in the production of D. alata seedlings for increasing seedling quality and resilience to water deficit.
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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) | 0.001 | 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".