Bibliographic record
Abstract
SEQUENCING A GENOME IS just the first step in understanding the biology of an organism. Proteins really do the work. With that in mind, two groups independendy report similar methods for discovering protein complexes within yeast. Both groups use "bait" proteins to fish out protein interactions. Yeast proteins are turned into bait by attaching a tag that allows them to be captured in an irnmunoaffinity purification. The captured complexes are then separated by gel electrophoresis, and excised gel spots are analyzed by mass spectrometry (MS). A team of scientists from MDS Proteomics in Toronto and Odense, Denmark; Mount Sinai Hospital inToronto; and the University of Toronto used the method to detect 3,617 protein interactions, starting with 725 bait proteins [Nature, 415,180 (2002)}. The other team, led by Giulio Superti-Furga, vice president of biology at Cellzome AG and team leader at the European Molecular Biology Laboratory both in Heidelberg, Germany identified 232 distinct multiprotein complexes ...
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.028 |
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".