<scp>ISAC</scp> Probe Tag Dictionary: Standardized Nomenclature for Detection and Visualization Labels Used in Cytometry and Microscopy Imaging
Bibliographic record
Abstract
Since the advent of microscopy imaging and flow cytometry, there has been an explosion in the number of probes, consisting of a component binding to an analyte and a detectable tag, to mark areas of interest in or on cells and tissue. Probe tags have been created to detect and/or visualize probes. Over time, these probe tags have increased in number. The expansion has resulted in arbitrarily created synonyms of probe tags used in publications and software. The synonyms are problematic for readability of publications, accuracy of text/data mining, and bridging data from multiple platforms, protocols, and databases for Big Data analysis. Development and implementation of a universal language for probe tags will ensure equivalent quality and level of data being reported or extracted for clinical/scientific evaluation as well as help connect data from many platforms. The International Society for Advancement of Cytometry Data Standards Task Force composed of academic scientists and industry hardware/software/reagent manufactures have developed recommendations for a standardized nomenclature for probe tags used in cytometry and microscopy imaging. These recommendations are shared in this technical note in the form of a Probe Tag Dictionary. © 2020 International Society for Advancement of Cytometry.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".