Seasonal variation of leaf ecophysiological traits of Iris variegata observed in two consecutive years in natural habitats with contrasting light conditions
Bibliographic record
Abstract
The amount and pattern of individual phenotypic responses to seasonal changes in environmental conditions were determined in clones of Iris variegata growing in differing light habitats. For the purpose of the study, 97 clonal plants of the rhizomatous herb I. variegata that experienced different light conditions in their two native habitats were selected: one along the top and slope of sand dunes and one in woodland understories. Two fully expanded leaves that had developed during spring, summer and fall in two consecutive years were sampled from each of these clones. Six leaf traits affecting the photosynthetic rate of a plant ? morphological (specific leaf area), anatomical (stomatal density) and physiological (total chlorophyll concentration, chlorophyll a/chlorophyll b ratio, carotenoid concentration, chlorophyll a/carotenoid ratio) exhibited significant plastic responses in the two different light habitats. To test whether these traits differ between exposed and shaded habitats as well as during different vegetation periods, we used the repeated model analysis of variance (ANOVA). Results of the repeated ANOVA revealed statistically significant effects of year, habitat and period of vegetation season. Patterns of changes during growing seasons were year-specific for almost all analyzed traits.
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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 | 0.000 |
| 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".