Germination of tropical legume seeds from Brazilian seasonally dry environments: effects of alternating temperature and water stress
Bibliographic record
Abstract
Legumes dominate many tropical landscapes, often producing water-impermeable seeds (physical dormancy, PY). The present study evaluated (1) whether an alternating temperature regime (AT; 20–40 °C) enhance germination, mainly by promoting PY-break, and (2) the effect of reduced water potentials ( Ψ = 0.0, −0.4, −0.8, −1.2 MPa) on germination of legumes from Brazilian seasonally dry environments. Experiment 1 involved all six study species, which differed in the fractions of nondormant (ND) and PY seeds, comparing a constant room temperature (CT, 25 °C; 30 days) with a 2-week AT followed by other 2 weeks of constant conditions. Experiment 2 tested four species (ND or with only a fraction of PY) under water stress (for 30 days), followed by stress relief and monitoring of germination recovery for 15 additional days. AT did not enhance germination, but seeds mostly maintained their viability. Water stress strongly inhibited the germination of ND seed fractions, with post-stress germination recovery ranging from 18% to 49%. Mimosa caesalpiniifolia Benth. was an exception, showing high seed mortality. Overall, legume seeds may persist by withstanding thermal and hydric stress, enabling recruitment under more favorable conditions. Such strategies likely contribute to regeneration in tropical seasonal environments, increasingly affected by warming and aridity due to climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".