A simple mathematical model to aid quantification of electrophoresis gels by image analysis
Bibliographic record
Abstract
In many scientific disciplines, measurements are taken from films that have been exposed to energetic sources. Examples include radiographs where the source is an X-ray tube, autoradiography where the source is a radioactive isotope and electrophoresis gels where the source is an enhanced chemiluminescence reaction. In these situations it is of interest to quantify the darkening of the film and compute the strength of the source which in the cases of autoradiography and electrophoresis can be used to compute unknown concentrations of biochemicals. We developed a simple mathematical model of the darkening of films in radiography, autoradiography and electrophoresis bands disclosed by enhanced chemiluminescence, and present formulae to calculate the strength of the source from measurement of film blackening by image analysis. A simple model is used in two examples to predict blackening of film exposed to electromagnetic radiation. This blackening is measured by image analysis. Results show reasonable agreement between predictions of the model and blackening of film for the examples chosen. This model is proposed as an aid to quantification of electrophoresis gels.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".