Possible roles for ethylene and gibberellin in the phenotypic plasticity of an alpine population of <i>Stellaria longipes</i>
Bibliographic record
Abstract
Four phenotypically different genotypes from an alpine population of Stellaria longipes Goldie s.l. (Caryophyllaceae) were collected from neighbouring sites at the top of the Plateau Mountain in southeastern Alberta, Canada, to examine a possible hormonal basis for their differences in stem length, leaf size, and flowering characteristics. All four genotypes had a dwarf shoot phenotype, compared with the low-elevation ecotype. Among the four genotypes, PMI-D was the tallest and had the largest leaves and flowers as well as more flowers per plant. PMI-D also maintained the flowering state, upon repropagation, without low temperature, short-day vernalization. Under controlled long-day warm conditions, the PMI-D genotype had a higher rate of ethylene evolution, but contained levels of endogenous gibberellin A1 that were similar to the other three (smaller) alpine genotypes. PMI-D was more sensitive to exogenously applied ethylene and growth-active gibberellins than other alpine genotypes. In contrast, the other three genotypes were smaller, had fewer (and smaller) flowers, and exhibited low ethylene evolution and a reduced sensitivity to applied ethylene and growth-active gibberellins. Speculatively, this behaviour may indicate an adaptation within this unique population of “dwarf” phenotypes that involves enhanced sensitivity to endogenous ethylene and gibberellins.
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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".