Bibliographic record
Abstract
The University of Saskatchewan (UofS) has been conducting crosses of Lonicera caerulea and releasing genotypes for fruit production under the name “Haskap”. The primary objectives of the UofS Haskap Breeding Program are to improve fruit flavor, increase fruit size and facilitate mechanical harvest. A more recent additional objective is to increase the concentration and content of compounds with the potential to enhance human health in the fruit and leaves of new haskap genotypes.As a first step to meet this additional objective, this project surveyed the secondary metabolites present within fruit and leaves of haskap. Genotypes tested included genotypes released by the UofS Haskap Breeding Program, unnamed genotypes with potential for use in breeding programs, genotypes acquired from germplasm repositories as well as genotypes of Lonicera caerulea subsp. villosa. Secondary metabolites were selected for further study if they were both linked to human health and were found at sufficient concentrations in haskap to allow for quantification. Chlorogenic acid, quercetin (three glycosides), loganin and secologanin matched these selection criteria. HPLC and mass spectrometry methodologies were developed to allow for quantification and identification of the target secondary metabolites in methanolic extracts of haskap fruit and leaves. Concentrations of the selected secondary metabolites decreased with fruit development, but the overall content (concentration x fruit weight) increased. In fruit tested at harvest maturity, the highest concentrations of many compounds of interest occurred in the widely grown cultivar Tundra. The concentrations of secondary metabolites in haskap leaves also decreased over the growing season, however at the end of the season, substantial amounts of secondary metabolites were still present in the leaves. The fruit and leaves of Lonicera caerulea subsp. villosa germplasm had a different quercetin profile than the other Lonicera caerulea genotypes surveyed. The concentrations of secondary metabolites in the fruit of the various genotypes were negatively correlated with the individual fruit weight produced by each genotype. The concentrations of some of the metabolites in fruit and leaves of Lonicera caerulea subsp. villosa also varied with the geographic site of origin of the genotypes. Post-harvest treatment of haskap fruit with UVC did not enhance the secondary metabolite profile.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".