Bibliographic record
Abstract
Mass cytometry imaging is a technique that utilizes mass spectrometry instead of fluorescent flow cytometry to detect metal-conjugated antibodies binding to single cell antigens. This method generates multi-dimensional images, a complex data type with significant computational and interpretation challenges due to the absence of a standardized analysis workflow. To address this, our project evaluated the quality and efficacy of a previous study’s proposed workflow to advance the interpretability and accuracy of mass cytometry data analysis. The approach evaluated whether the previously proposed workflow could effectively analyze alternative samples classified by previous studies as “mixed” immune structure. The methods included an in-depth examination of cell labeling based on spatial proximity, which emerged as a secondary analytical outcome. This research advances spatial labeling by enhancing the spatial information related to physical connections between interacting cells and incorporating biological interactions, such as protein involvement. By integrating these factors, this research proposes a method for more accurate labeling, contributing to a more robust analysis workflow for this complex data type. Unstructured “mixed” samples remained distinguishable, though less effectively than structured “compartmentalized” samples. Future applications involve exploring additional datasets to further develop means of distinguishing between contact and non-contact interactions among cancer and immune cells.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".