Bibliographic record
Abstract
For bacteria facing a dose of antibiotics, timing might be the key to evading destruction. In a series of experiments, Princeton researchers found that cells that repaired DNA damaged by antibiotics before resuming growth had a much better chance of surviving treatment. [19] It's called gene editing, and University of Alberta researchers have just published a game-changing study that promises to bring the technology much closer to therapeutic reality. [18] An LMU team now reveals the inner workings of a molecular motor made of proteins which packs and unpacks DNA. [17] Chemist Ivan Huc finds the inspiration for his work in the molecular principles that underlie biological systems. [16] What makes particles self-assemble into complex biological structures? [15] Scientists from Moscow State University (MSU) working with an international team of researchers have identified the structure of one of the key regions of telomerase—a so-called cellular immortality ribonucleoprotein. [14] Researchers from Tokyo Metropolitan University used a light-sensitive iridium-palladium catalyst to make sequential polymers, using visible light to change how building blocks are combined into polymer chains. [13] Researchers have fused living and non-living cells for the first time in a way that allows them to work together, paving the way for new applications. [12] UZH researchers have discovered a previously unknown way in which proteins interact with one another and cells organize themselves. [11] Dr Martin Sweatman from the University of Edinburgh's School of Engineering has discovered a simple physical principle that might explain how life started on Earth. [10] Nearly 75 years ago, Nobel Prize-winning physicist Erwin Schrodinger wondered if the mysterious world of quantum mechanics played a role in biology. A recent finding by Northwestern University's Prem Kumar adds further evidence that the answer might be yes. [9]
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads 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".