Bibliographic record
Abstract
W e hope that you had a good summer and we are very excited to share with you that we have received our first impact factor of 2.7 and a cite score of 3.3.We thank both you as writers, reviewers, and readers of PHAGE and our incredibly hard working behind the scenes folk at Mary Ann Liebert who have made this happen.This is a great achievement for a new journal and shows the appetite for the content all of us are generating and curating in PHAGE.Hopefully you have caught at least one of the inspiring phage conferences that were hosted over this period in Paris in June for the World Congress on targeting phage therapy; in Tbilisi, Georgia, in July-for the Viruses of Microbes meeting; in Evergreen, Olympia, United States, in August for the International Phage meeting; or the Bacteriophage meeting in September in Oxford.All meetings had their own unique flavor, were beautifully organized and the sun shone!Importantly there was a consistently high standard of presentations on all topics often motivated by a desire to use phages in therapy and a plethora of approaches to better understand phages, and phage behavior in complex systems.The meetings had a common positivity, as evidenced increasing clinical presence accompanied by methodological advances to allow better understanding of phages.Anyway, as summer turns to autumn, we are happy to bring this September Edition of PHAGE to you.We are pleased to include a systematic review of how to manage urinary tract infections or UTIs.UTIs are a major global problem-and several research groups worldwide are working on different aspects of how to best use phage to treat these difficult infections and in doing so prevent bloodstream infections.In a great effort to bring together information on how effective this approach might be, Amany M. Al-Anany across Ontario, Canada, has examined the 55 studies that can be found in the literature.This includes studies on multiple different species and studies from older literature and from articles that needed to be translated into English.These both serve as a very useful review in this topic and can form a blueprint to future reviews for different indications that are being investigated for phage therapy.We hope you enjoy the illustrative cartoon on the same topic, drawn by Ellie Jameson.
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 | 0.053 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".