Particle and Microorganism Enumeration Data: Enabling Quantitative Rigor and Judicious Interpretation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many of the methods routinely used to quantify microscopic discrete particles and microorganisms are based on enumeration, yet these methods are often known to yield highly variable results. This variability arises from sampling error and variations in analytical recovery (i.e., losses during sample processing and errors in counting), and leads to considerable uncertainty in particle concentration or log(10)-reduction estimates. Conventional statistical analysis techniques based on the t-distribution are often inappropriate, however, because the data must be corrected for mean analytical recovery and may not be normally distributed with equal variance. Furthermore, these statistical approaches do not include subjective knowledge about the stochastic processes involved in enumeration. Here we develop two probabilistic models to account for the random errors in enumeration data, with emphasis on sampling error assumptions, nonconstant analytical recovery, and discussion of counting errors. These models are implemented using Bayes' theorem to yield posterior distributions (by numerical integration or Gibbs sampling) that completely quantify the uncertainty in particle concentration or log(10)-reduction given the experimental data and parameters that describe variability in analytical recovery. The presented approach can easily be implemented to correctly and rigorously analyze single or replicate (bio)particle enumeration data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it