Consistency of phenolic profiles with taxonomic distribution and adaptation of birch species (<i>Betula</i> L.) to environmental conditions
Bibliographic record
Abstract
The phenolic compounds in the leaves of 12 species of birch trees of the subgenera Neurobetula, Betulenta, and Betula were biochemically profiled using HPTLC. The duration of the vegetation period was found to be significantly related to the content of total phenols ( r = 0.74) and flavonoids in leaves ( r = 0.65). The correlations for Neurobetula plants were 0.86 and 0.91, respectively. The relationship between the duration of the growing season and the concentration of phenolic compounds in Betula plants was inverse ( r = −0.84). A cluster analysis of phytochemical profiles revealed that the studied birch species form groups that coincide with the subgenera proposed by De Jong due to an affinity with the qualitative composition of phenolic compounds. A multiple correlation analysis confirmed the relationship between the qualitative composition of phenolic compounds and the morphological characteristics of the leaves. The results of phytochemical profiling revealed that the qualitative composition of polyphenols in the leaves of 12 birch species is quite specific, allowing the use of individual compounds as additional differential biochemical characters in identifying species and hybrids and studying their potential role in plant adaptation to habitat conditions.
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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 | 0.002 | 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".