Sampling variability and the design of bacterial abundance and production studies in aquatic environments
Bibliographic record
Abstract
Published data for aquatic bacterial abundance and production in benthic and planktonic environments were collected from the literature to describe relationships between sample means and variances, to explore the factors that affect these relationships, and to estimate the number of samples needed to detect specified differences among means with adequate power. Between 75 and 94% of sample log10(variance) was explained by log10(mean) for both bacterial abundance and production. Differences in mean-variance relationships of bacterial abundance and production due to habitat (river, lake, marine), quantification method, and experimental manipulation (planktonic bacteria) or substrate type (benthic bacteria) were negligible (less than 11% of residual variance from regressions explained). Between 12 and 69 replicates are necessary to detect a 20% difference in means for bacterial abundance and production with a power of 80%. Given the median rate of replication of 3 to 4, the majority of published studies reviewed here are, at best, able to detect differences in means of 50% (planktonic bacterial abundance) or 100% (planktonic production and benthic abundance and production) with 80% power. If effect sizes less than these values are deemed biologically meaningful, then future studies will have to increase sampling effort to enable detection of such differences.
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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 | 0.211 | 0.342 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".