A critical path to producing high quality, reproducible data from quantitative western blot experiments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Western blotting experiments were initially performed to detect a target protein in a complex biological sample and more recently, to measure relative protein abundance. Chemiluminescence coupled with film-based detection was traditionally the gold standard for western blotting but accurate and reproducible quantification has been a major challenge from this methodology. The development of sensitive, camera-based detection technologies coupled with an updated technical approach permits the production of reproducible, quantitative data. Fluorescence reagent and detection solutions are the latest innovation in western blotting but there remains questions and debate concerning their relative sensitivity and dynamic range versus chemiluminescence. A methodology to optimize and produce excellent, quantitative western blot results with rigorous data analysis from membranes probed with both fluorescent and chemiluminescent antibodies is described. The data reveal when and how to apply these detection methods to achieve reproducible data with a stepwise approach to data processing for quantitative analysis.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| 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 it