Better management of <scp>W</scp>estern blotting results using professional photo management software
Bibliographic record
Abstract
Western blotting is a proven technique essential to a significant proportion of molecular biology projects. However, as results accumulate over the years, managing data can become daunting. Recognizing that the needs of a scientist working with Western blotting results are conceptually the same as those of a professional photographer managing a summer's worth of wedding photos, we report here a new workflow for managing Western blotting results using professional photo management software. The workflow involves (i) scanning all film-based results; (ii) importing the scans into the software; (iii) processing the scans; (iv) tagging the files with metadata, and (v) creating appropriate "smart-albums." Advantages of this system include space savings (both on our hard drives and on our desks), safer archival, quicker access, and easier sharing of the results. In addition, metadata-based workflows improve cross-experiment discovery and enable questions like "show me all blots labelled with antibody X" or "show me all experiments featuring protein Y". As project size and breadth increase, workflows delegating results management to the computer will become more and more important so that scientists can keep focussing on science.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.201 | 0.132 |
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".