The Comparative Analysis of Agronomic Characters and Phenolic Composition of Blue Honeysuckle Berries (<i>Lonicera caerulea</i> L.) Cultivated in Central Yakutia
Bibliographic record
Abstract
This study investigates the agronomic characteristics and phenolic composition of ten varieties of blue honeysuckle (Lonicera caerulea L.) cultivated in Central Yakutia. The research was prompted by the nutritional needs of the local population, particularly during the long winters when vitamin sources are scarce. The varieties included in the study were bred at various research institutes, with 'Goluboe Vereteno' serving as a standard due to its recommendation for the region. The study was conducted over the 2022-2023 vegetation seasons in Pokrovsk, Yakutia, focusing on identifying promising varieties that exhibit high yield, favorable taste, and significant polyphenolic content, which are essential for nutritional benefits. The findings highlight the potential of honeysuckle as a valuable crop in the region, contributing to local food security and agricultural diversity. The research underscores the growing interest in honeysuckle cultivation, which has gained traction in various countries, including Canada and Japan, where it is recognized for its health benefits and adaptability to different climates. Overall, this study contributes to the understanding of honeysuckle's agronomic potential in extreme northern conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".