ScatterJ: An ImageJ plugin for the evaluation of analytical microscopy datasets
Bibliographic record
Abstract
We present ScatterJ, an ImageJ plugin that allows for extracting qualitative as well as quantitative information from analytical microscopy datasets. A large variety of analytical microscopy methods are used to obtain spatially resolved chemical information. The resulting datasets are often large and complex, and can contain information that is not obvious or directly accessible. ScatterJ extends and complements existing methods to extract information on correlation and colocalization from pairs of species-specific or element-specific maps. We demonstrate the possibilities to extract information using example datasets from biogeochemical studies, although the plugin is not restricted to this type of research. The information that we could extract from our existing data helped to further our understanding of biogeochemical processes such as mineral formation or heavy metal sorption. ScatterJ can be used for a variety of different two-dimensional (2D) and three-dimensional (3D) datasets such as energy-dispersive X-ray spectroscopy maps, 3D confocal laser scanning microscopy maps, and 2D scanning transmission X-ray microscopy maps.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.015 |
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".