Features www.BioTechniques.com111Vol. 56 | No. 3 | 2014 Tech News
Bibliographic record
Abstract
Imagine you’re just starting in a lab. Your new PI decides to test your mettle at the bench with a simple project: Replicate some immunohistochemistry results from a recent publication. No problem, right? Not necessarily. Immunohistochemistry (IHC) relies on antibodies, and antibodies, says Anita Bandrowski of the University of California, San Diego, “are extremely messy. ” Unlike many reagents in today’s molecular biology lab, antibodies are far from the turnkey solutions commercial vendors would have you believe. Antibodypedia.com lists 1.2 million antibodies in its database. Some work in Western blots but not in IHC, others can precipitate protein complexes but come up empty in flow cytometry, and some don’t work at all. More than a quarter of 246 histone modification antibodies tested in a 2011 study were found to be non-specific; of those that were specific, 22 % were unsuitable for chromatin immunoprecipi-tation (1). Perhaps more alarmingly, some antibodies work, but recognize the wrong target. In that 2011 study, 4 antibodies “showed 100 % specificity, but for the wrong [histone] peptide, ” the authors reported. “We have talked to a lot of researchers who say this is actually one of the single biggest problems that they’ve experienced with reagents in the lab, ” says Elizabeth
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.446 | 0.563 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".