Bibliographic record
Abstract
Iron is an essential nutrient, yet iron balance is precarious for much of the world’s population. In North America, iron deficiency is common in distinct population groups, such as women of child-bearing age. 1 Transfusion therapy is a cornerstone of modern medical care. Canada’s need for fresh blood components, including red blood cells, is met by approximately 600,000 volunteer donors. Canadian Blood Services (CBS) collects, processes, tests, and distributes blood components to all provinces and territories, except Quebec. Each year, CBS collects approximately 950,000 units of whole blood of 500 mL each. A dedicated group of repeat donors provide close to 90% of donations, while 10% of donations come from first time donors. The average donation frequency in 2012 was slightly over 2 donations per donor yearly. Donor hemoglobin (Hb) screening is performed on a fingerstick capillary sample prior to each donation, with a minimum qualifying level of 125 g/L required for both male and female donors, and 8% of female donors and 0.5% of male donors are temporarily deferred for inadequate Hb level. Each whole blood donation results in the loss of 225 to 250 mg of iron. However, no routine Key points
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".