Germination characteristics of <i>Gymnocalycium monvillei</i> (Cactaceae) along its entire altitudinal range
Bibliographic record
Abstract
Germination characteristics are important for understanding how species cope with environmental variation. The aims of this work were to analyze the effect of different temperatures (25 and 32 °C), water potentials (0, −0.2, −0.4, and −0.6 MPa), and light conditions (light vs. darkness) on the germination of five populations of the cactus Gymnocalycium monvillei (Lem.) Britton & Rose along its entire altitudinal distribution. The experiments to assess the effects of temperature, water potential, and light conditions were performed in germination chambers, and total germination (%) and mean germination time (T 50 ) were recorded. Germination decreased in provenances from higher to lower altitudes, and the effect was very pronounced at temperatures of 32 °C. For all of the altitudinal provenances, germination decreased with lower water potential, with this effect being more pronounced at 32 °C. On the other hand, provenances at lower altitudes were less affected by lower water potentials than higher provenances. Provenances at all altitudes showed very low germination under dark conditions. T 50 did not vary among altitudinal provenances at a temperature of 25 °C, but at 32 °C germination was slower at intermediate altitudes. Our results show that germination characteristics differ considerably among altitudinal provenances and seem to be important in determining the capacity of the species to inhabit such a broad gradient.
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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 | 0.001 | 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".