Seed mass modulates tolerance to water deficit in Luetzelburgia auriculata (Allemão) Ducke seedling
Bibliographic record
Abstract
ABSTRACT In semi-arid regions, water deficit occurs for long periods, but the adaptation mechanisms developed by endemic species are little known. The objective of this study was to determine the tolerance of Luetzelburguia auriculata (Allemão) Ducke seedlings to water stress regarding germination and seedling establishment related to seeds’ mass. The experiment was conducted in a completely randomized design in a 2 x 5 factorial scheme, with two classes of seeds (light < 0.35 g and heavy ≥ 0.35 g) and five osmotic potential levels (-0.2; -0.4; -0.6; -0.8 and -1.0 MPa), distributed in four replications of 25 seeds per plot. The following parameters were evaluated: germination rate (GR), first germination count (FGC), germination speed index (GSI), mean germination time (MGT), root length (RL), fresh and cotyledon dry mass (CFM/CDM) and fresh and dry root mass (RFM/RDM). As the osmotic potential became more negative, there was a reduction in physiological variables. The heavy seeds (≥ 0.35 g) showed increases when compared to light seeds (<0.35 g), of 15.73, 22.94, 15.02, 23.33, 31.43, 21.43% for the variables GR, FGC, RL, CFM, CDM and RFM, respectively. Therefore, heavy seeds are more tolerant to water stress and should be prioritized for the recovery of degraded areas, especially in places with low rainfall.
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) | 0.001 | 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".