Metamorphosis: the Coming Transformation of Translational Systems Biology
Bibliographic record
Abstract
One morning, as Gregorina Samsa was waking up from anxious dreams, she discovered that she had become afflicted with certain mysterious flu-like symptoms that appeared without any warning. Equally irritating, this capricious metamorphosis seemed impervious to a rational explanation in terms of causes and effects. "What’s happened to me?" she thought. Before seeing a doctor, she decided to find out more about what might ail her. She logged on to a Web site where she annotated a timeline with what she could remember. Since March, she’d had more headaches than usual, and then in April she had begun to experience more fatigue after exercise, and as of July she had also experienced occasional lapses in memory. "Why don’t I go back to sleep for a little while longer and forget all this foolishness," she thought. As she was about to abandon this errand, the system came back to life with a barrage of questions: Is she female? Had she experienced any significant stress in the past few months? Had she noticed any joint or muscle pain? It also obtained her permission to download her genomic profile.
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 | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".