Enhanced Reproducibility and Precision of High-Throughput Quantification of Bacterial Growth Data Using a Microplate Reader
Bibliographic record
Abstract
This study aimed to develop a repeatable, reliable, high-throughput protocol to monitor bacterial growth in 96-well plates and analyze the maximum growth rate. The growth curves and maximum growth rates of two bacterial species were determined. Issues including (i) lid condensation, (ii) pathlength correction, (iii) inoculation size, (iv) sampling time interval, and (v) spatial bias were investigated. The repeatability of the protocol was assessed with three independent technical replications, with a standard deviation of 0.03 between the runs. The maximum growth rates of Bacillus mycoides and Paenibacillus tundrae were determined to be (mean ± SD) 0.99 h−1 ± 0.03 h−1 and 0.85 h−1 ± 0.025 h−1, respectively. These bacteria are more challenging to monitor optically due to their affinity to clump together. This study demonstrates the critical importance of inoculation size, path length correction, lid warming, sampling time intervals, and well-plate spatial bias to obtain reliable, accurate, and reproducible data on microplate readers. The developed protocol and its verification steps can be expanded to other methods using microplate readers and high-throughput protocols, reducing the researchers' innate errors and material costs.
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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 | 0.025 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".