Antibiotics under our feet: investigating microbial sources of antibiotics in our urban soil samples
Bibliographic record
Abstract
This dataset consists of a Krona chart visualisation of our bioinformatics results for our first six soil samples sent for DNA sequencing. The Krona chart was used to determine if microbes known to be sources of antibiotics were present in our soil samples. The two publications referenced below were used to obtain the names of such microbes. A spreadsheet of the results together with UK prescribing information from the BNF about the antibiotic drug arising from each microbes souce is included. An table to display the results was produced and is presented here in .mp4 video format that can be paused to view results for each soil sample. The data analysis for this part of the "Antibiotics under our feet" was carried out in collaboration with a Nuffield Research Placement student at the University of St Andrews during the summer of 2022. The Nuffield project protocol with instructions for analysing the Krona chart to gather data is included. References: Matthew I Hutchings, Andrew W Truman, Barrie Wilkinson, Antibiotics: past, present and future, Current Opinion in Microbiology, Volume 51, 2019, Pages 72-80, ISSN 1369-5274, https://doi.org/10.1016/j.mib.2019.10.008 (https://www.sciencedirect.com/science/article/pii/S1369527419300190) Procópio, Rudi & Silva, Ingrid & Martins, Mayra & Azevedo, Joao & Araújo, Janete. (2012). Antibiotics produced by Streptomyces. The Brazilian journal of infectious diseases : an official publication of the Brazilian Society of Infectious Diseases. 16. 466-71. 10.1016/j.bjid.2012.08.014.
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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 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".