Bibliographic record
Abstract
Chapter 2. "Nramp1 Equips Macrophages for Efficient Iron Recycling." [published inExperimental Hematology] I helped to conceive of this study, performed the majority of the experiments, analyzed the data, interpreted the data, and wrote the manuscript.Alex Sheftel also helped to conceive of this study and provided conceptual contributions.Brandi Wasyluk performed experiments, and Prem Ponka provided significant conceptual contributions to the study, interpreted the data, and helped edit the manuscript.Chapter 3. "Nramp1 promotes the efficient macrophage recycling of iron following erythrophagocytosis in vivo."[published in the Proceedings of the National Academy of Sciences] I helped to conceive of this study, performed the majority of the experiments, interpreted the data, and wrote the manuscript.Sameer Apte assisted with the experiments, and helped to revise the manuscript.Billy Andriopoulos measured hepcidin levels, provided conceptual assistance, and helped to revise the manuscript.Marc Andrews performed the staining experiments.Matthias Schranzhofer, Tanya Kahawita and Daniel Santos performed the supplementary flow cytometry experiments.Prem Ponka helped to conceive of this study, interpreted the data, provided significant conceptual assistance, and helped to edit and revise the manuscript.Chapter 4. "Both Nramp1 and DMT1 are necessary for the recycling of erythrocyte derived iron macrophages."[submitted to Blood] I helped to conceive of this study, performed the majority of the experiments, analysed and interpreted the data, and wrote the manuscript.Marc Mikhael contributed to the conceptual aspects of the study, and performed some of the preliminary western blots.Sameer Apte assisted with the experiments, and helped edit the manuscript.Guangjun Nie provided invaluable assistance with the siRNA knockdown experiments, Lila Kayembe performed the 59 Fe-Tf control experiments, and with Daniel Santos, performed the HO-1 activity assay.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".