Morphological variability of Quercus robur L. leaf in Serbia
Bibliographic record
Abstract
This paper presents the results of a study dealing with leaf morphological variability of Quercus robur L. 148 trees were sampled from 5 population across Serbia and 17 morphological traits were assessed. Interpopulation variability was confirmed by the results of the analysis of variance (ANOVA). A statistically significant (p <0.05) effect of population was obtained for most of the studied morphological characters. Intrapopulation variability was confirmed by statistically significant tree effects for all of the studied leaf characters (all p < 0.05). The results of the multivariate analysis of variance (MANOVA) confirmed a significant population and tree share in the total phenotypic variability (all p <0.05). By applying the canonical discriminant analysis (CDA), the first discriminant function accounted for 63% of the variability between populations and the second accounted for 20% of the population variability. The leaf area (AREA), specific leaf area (SLA) and surface area to perimeter ratio (ARPE) had the greatest effect on population differentiation (CDA). It is assumed that different environmental conditions affect population differentiation and that high intrapopulation variability is due to intraspecific variability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.005 | 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 teacher head, 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".