The Close-Call in Microbiology Labs - Understanding Microbes
Bibliographic record
Abstract
For a sophomore student, new to microbiology lab, most of the microbial colonies look alike -kind of a toddler's view on a dog or a cat; both have 4 legs and a tail, and now what?!The students level of confidence increases within a few weeks as they learn to identify the morphology -the form, the elevation, the margin and the list goes on, followed by the same protocol of testing antimicrobial susceptibility using antibiotic discs and measuring the zone of inhibition with a ruler.My question: Why not include a more relevant approach to learn techniques which demonstrates that the same microbe responds differently based on the form in which they exist; planktonic or biofilm -either relating to clinical biomaterials like stents in human body and its antibiotic treatments or biofilms in Lake Ontario water supply channels and chlorine/ disinfectant treatment as related to the environment.This will enhance their learning on current real world applications, as in medical, scientific or pharmaceutical settings.This paper discusses a study based on determining the Minimum Inhibitory Concentration, Minimum Biofilm Eradication Concentration using the antibiotics neomycin sulfate and co-trimoxazole on the opportunistic pathogens Citrobacter freundii and Aeromonas hydrophila as model organisms, using cost effective 96 wells plates.The results showed a trend indicating higher antibiotic concentrations and increased biofilm elimination, concluding the need of a higher concentration for biofilm eradication rather than the bare minimum concentration of 1mg mL -1 antibiotics used in the study.Firstpage
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.169 | 0.122 |
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".