Office of Research, Economic Engagement and Outreach Update, March 13, 2024
Bibliographic record
Abstract
ANNOUNCEMENTS New Flow CytometerFor several years, the University Instrumentation Center has housed and managed two flow cytometers: one with two lasers, able to measure six parameters (forward scatter, side scatter, four fluorescence emissions), and one with four lasers, able to measure eight parameters (forward scatter, side scatter, six fluorescence emissions).Now, thanks to our friends at Dartmouth College and funding from the OSVPREEO and the Center of Integrated Biomedical and Bioengineering Research (CIBBR), a third flow cytometer has been added to the UIC that is able to measure ten parameters (forward scatter, side scatter, eight fluorescence emissions).In addition, this Miltenyi VYB flow cytometerhas many automated features, including autosampling from 5mL tubes or 96-well plates, calibration, compensation, antibody labeling, staining, dilution, and cleaning.Its syringe-driven sample uptake helps prevent clogs and allows accurate volumetric cell counts to be made, difficult to achieve with standard sample injection ports like those on our other cytometers.The Miltenyi instrument also contains a cell enrichment unit to perform pre-analysis concentrating of rare cell types, fully automated, to speed up analysis of such cells.Contact Mark Townley for more details and to arrange training.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".