Bibliographic record
Abstract
Bacteriophages, viruses specific to bacteria, regulate bacterial communities in all known microbial systems. My research aims to determine how they interact with the trillions of bacteria found in the human gut. To do this, I apply a whole-systems perspective on both communities, considering bacteriophage replication cycles, bacterial and bacteriophage diversity, temporal dynamics, interactions, and responses to perturbations. Building upon ecological concepts and an array of independent and complementary techniques, I study the human gut from a microbial perspective. In contrast with bacteria, working with bacteriophage communities comes with many significant challenges, starting with the limited experimental toolkit and curated databases. Yet an increasing number of studies are pushing these scientific boundaries every day. In the human gut, future research that includes bacteriophages will lead to many exciting and warranted research avenues. From phage therapy and immunization to targeted drug delivery, the sky is the limit in my opinion.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".