A review of potential methods of determining critical effect size for designing environmental monitoring programs
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
The effective design of field studies requires that sample size requirements be estimated for important endpoints before conducting assessments. This a priori calculation of sample size requires initial estimates for the variability of the endpoints of interest, decisions regarding significance levels and the power desired, and identification of an effect size to be detected. Although many programs have called for use of critical effect sizes (CES) in the design of monitoring programs, few attempts have been made to define them. This paper reviews approaches that have been or could be used to set specific CES. The ideal method for setting CES would be to define the level of protection that prevents ecologically relevant impacts and to set a warning level of change that would be more sensitive than that CES level to provide a margin of safety; however, few examples of this approach being applied exist. Program-specific CES could be developed through the use of numbers based on regulatory or detection limits, a number defined through stakeholder negotiation, estimates of the ranges of reference data, or calculation from the distribution of data using frequency plots or multivariate techniques. The CES that have been defined often are consistent with a CES of approximately 25%, or two standard deviations, for many biological or ecological monitoring endpoints, and this value appears to be reasonable for use in a wide variety of monitoring programs and with a wide variety of endpoints.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it