Measuring Iron Bioavailability in Peas via Cell
Bibliographic record
Abstract
Field pea is a valuable crop for delivery of high protein content, slowly digested carbohydrates, fiber, and a high density of vitamins and minerals, including iron. High iron levels are of particular importance in human diets, as anemia is an ongoing challenge for many individuals. Iron levels in seeds at harvest are mitigated by nutrient levels in the soil and crop genetics. However, although high iron levels may be measured in some pea varieties, there may be limited absorption during digestion due to presence of the naturally occurring plant molecule phytate, which chelates with iron, zinc, and other cations. The Warkentin team, at the University of Saskatchewan, have bred agronomically viable pea lines that are low in phytate. Collaborating with scientists at Cornell University, Ithaca, NY, these lines were tested for iron bioavailability by the Caco-2 cell culture assay and a chicken feeding study. This talk will summarize the research to date and share plans for upcoming human trials involving endurance-trained women and Paralympic athletes, two groups particularly prone to anemia. Link to Video Presentation: https://youtu.be/Y__lg9K3a3I
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".