No magic number: determining cost-effective sample size and enumeration effort for diatom-based environmental assessment analyses
Bibliographic record
Abstract
Aquatic microorganisms are commonly used as indicators in environmental assessments. Two key aspects of sampling design for these studies are the number of sites sampled and the enumeration effort of organisms within samples. However, there has been no rigorous determination of a cost-effective balance between them. Here, we use regional diatom data sets from 207 to 493 lakes to determine the influence of sample size (lake number) and enumeration effort (valve count) on the accuracy and cost-effectiveness of commonly used environmental assessment analyses. We find that both lake number and valve count can be considerably reduced from the original data sets. However, there is considerable variation among analyses, such that a single most cost-effective sampling configuration cannot be recommended. For weighted-averaging assemblage versus environment models, we recommend 70–100 lakes and 200 valves for retaining high accuracy. For other analyses, we recommend iteratively building up data sets, using initially high (300 or more) and consistent valve counts and using subsampling to determine when the influence of either lake number or valve count plateaus. This technique is applicable to other types of microorganisms used in environmental assessments.
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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 | 0.031 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".