An introduction to differential display and related techniques
Bibliographic record
Abstract
Abstract It is worth acknowledging, as there is no way around it, that differential display has been, and remains, a controversial technique. Any technique that engenders articles with the word ‘dismay ‘ in the title (1) must be controversial! More than a thousand articles, however, have now been published describing results achieved with the use of the differential display technique and hundreds of genes have been ‘discovered ‘. This book has been written with the firm belief that much of the scepticism that differential display has engendered is based upon a frequent poor understanding of the technique and the premature publication of many studies. Such studies have often been undertaken by groups who were early to recognize the power of this technique in biological applications but who had not yet achieved the necessary technical expertise to apply the technique successfully. Thus, in the heady days after the introduction of the technique by Liang and Pardee in 1992 (2), many biologists with divergent areas of expertise (neurobiology, psychology, plant sciences, pharmacology) rushed into applying the technique in their particular systems without, perhaps, a sufficiently solid background in modern molecular biology. This was a testi monial to the apparent simplicity of the technique and to the potential breadth of biological systems to which the technique might be applied.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.020 |
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".