Alaska Bog Blueberries: Isolation and Quantification of Components
Bibliographic record
Abstract
The goal of this study was to isolate a tentatively identified compound from Alaska Bog \n-- Blueberries in order to confirm its structure and determine the amount present in Alaska Bog \n-- Blueberry samples. Additionally, commercial blueberry samples were analyzed to compare the \n-- concentration of this compound between commercial blueberries and Alaska Bog Blueberries. \n-- The compound is of interest because it was shown to be biologically active in protecting cells \n-- from inflammation and oxidative stress based on assays developed in Dr. Kuhn’s lab (McGill, \n-- 2010). Additionally, the compound is the likely cause of the renowned tartness of Alaska Bog \n-- Blueberries. Ion-exchange chromatography was performed to separate this compound from the \n-- blueberry sample, 'ii NMR spectra were taken of the resulting product, demonstrating that \n-- fructose and other major impurities were removed. Separation of the compound and the \n-- remaining impurity, malic acid, was achieved through crystallization. A gated NMR was \n-- obtained of the isolated product, demonstrating that the isolated compound was citric acid, which \n-- was not the expected result. Quantification of the compound was done by analyzing blueberry samples on an HPLC, \n-- using Evaporative Light Scattering Detection. After determining that the compound was citric \n-- acid, a calibration curve was used to calculate the concentration o f citric acid in various \n-- blueberry samples. Alaska Bog Blueberries were determined to contain at least two to three \n-- times more citric acid than do commercially available berries, by dry weight. Over prolonged \n-- storage, the percentage o f citric acid appears to be decreasing by a significant amount, possibly due to water absorption. Another likely factor contributing to the variability of the Alaska Bog \n-- Blueberry samples is when the berries were picked.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".