Bibliographic record
Abstract
We thank Dr. Hoffer (1) for his interest in our work on vitamin C deficiency (2). In his letter, Dr. Hoffer questions the methods used for handling, storing, and analyzing our samples. He stated that we did not deproteinize our samples, but we indicate in Materials and Methods that “… 50 μL of salicylsalicylic acid were added as a deproteinizing agent …” (2, p. 465). We agree that ascorbic acid is more stable at −70°C for long-term storage; however, samples were stored at −20°C for less than 6 days, and plasma ascorbic acid has been shown to be stable under these conditions (3–5). Dr. Hoffer also questions the reliability of our high performance liquid chromatography method, yet we clearly described using certified controls from the National Institute of Standards and Technology (NIST). This is the universally accepted method of ensuring that an analytical technique is reliable, including the measurement of plasma ascorbic acid (6). A control sample from the NIST was run after calibrating and after every tenth sample analyzed, and the observed coefficient of variation ranged from 4.9% to 7.8%. Dr. Hoffer makes these assertions because he questions the proportion (∼50%) of our subjects with subnormal plasma vitamin C and points to the study by Gan et al. (7) (of which he is a coauthor), showing that only 13% of their reference outpatient population had subnormal plasma vitamin C concentrations. However, their study included subjects who were not fasting at the time of blood collection, which is a clear limitation in a study that aims to assess ascorbic acid status. Conflict of interest: none declared.
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 | 0.005 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.033 | 0.043 |
| Insufficient payload (model declined to judge) | 0.024 | 0.017 |
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".