Quantifying natural variability as a method to detect environmental change: Definitions of the normal range for a single observation and the mean of <i>m</i> observations
Bibliographic record
Abstract
The normal range has been defined as the range that encloses 95% of reference values; in practice this range has been defined as the reference mean ± 2 standard deviations (SD). When sample sizes are small and reference data are not normally distributed, the mean ± 2 SDs do not enclose 95% of data values. Prediction intervals (PI) calculated using sample statistics are used in the present study to define the normal range for a single observation and the mean of m observations. The PIs provide confidence limits for the next randomly selected observation (or mean of m observations) from a population. The PIs are defined using normally distributed reference data; normality can typically be achieved with transformations of the data. Covariates can be used to explain some of the variability in the reference distribution, increasing the ability to detect change. When assumptions of normality are not met, alternative methods of defining the normal range are provided. The normal range can be used to quantify natural variability and assess change from the reference distribution. It can be used as an early warning indicator of change in environmental monitoring to identify the need for further investigation.
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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 | 0.038 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".