Getting more from cell size distributions: Establishing more accurate biovolumes by estimating viable cell populations
Bibliographic record
Abstract
Current approaches for cell size distribution modeling are attempting to describe the behavior of the entire distribution with respect to time. Although some advances have been made in this area, the modeling process requires a large number of culture-specific parameters and an a priori assumption of the distribution nature (Poisson, Gaussian, etc.). In this work, we propose a deconvolution of the distribution into size ranges and an iterative regression process with respect to a single culture variable, such as viability. Following this approach, two example applications are outlined using data collected with a Coulter Counter Multisizer. In the first, traditional biovolume measurements are corrected to account for the noneven distribution of nonviable cells. These corrections amount to an average increase of 7-65% in the calculated biovolume from 24 to 72 h postinfection and are expected to aid in the development of a new basis for nutrient consumption postinfection. In the second example, viability is predicted from the cell size distribution using both linear and exponential regressions. Differences between predicted and measured viabilities were found to be normally distributed with means of 0.4% and 0% as well as standard deviations of 7.6% and 8.1% for linear and exponential regression, respectively. Although only viability relationships were tested, our approach yielded significant results for both applications, allowing the possibility for further development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".