Bibliographic record
Abstract
Lactobacillus were an incredibly large, diverse genus of bacteria comprising 261 species, several of which were commensal strains with the potential for use as a chassis for synthetic biological endeavors within the gastrointestinal tract. The wide phenotypic and genotypic variation observed within the genus led to a recent reclassification and the introduction of 23 novel genera. Due to the breadth of variations within the old genera, protocols demonstrated in one member may not work as advertised with other members. A lack of centralized information on how exactly to manipulate specific strains has led to a range of ad hoc approaches, often adapted from other bacterial families. This can complicate matters for researchers starting in the field, who may not know which information does or does not apply to their chosen strain. In this paper, we aim to centralize a set of protocols with demonstrated success, specifically in the Limosilactobacillus reuteri strain designation F275 (other collection numbers: DSM20016, ATCC23272, CIP109823), along with troubleshooting advice and common issues one may encounter. These protocols should enable a researcher with little to no experience working with L. reuteri DSM20016 to transform a plasmid, confirm transformation, and measure system feedback in a plate reader via a reporter protein.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.370 | 0.260 |
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".