Bibliographic record
Abstract
Understanding variability of population abundances is of central concern to theoretical and applied evolutionary ecology, yet quantifying the conceptually simple idea has been substantially problematic. Standard statistical measures of variability are particularly biassed by rare events, zero counts and other ‘non‐Gaussian’ behaviour, which are often inappropriately weighted or excluded from analysis. I conjecture that these problems are primarily a function of calculating variation as deviation from an average abundance, while the average may not be static, nor actually reflect abundance at any point in the time series. Here I describe a simple metric (population variability PV) that quantifies variability as the average percent difference between all combinations of observed abundances. Zero counts can be included if desired. Similar to standard metrics, variability is measured on a proportional scale, facilitating comparative applications. Standard metrics are based on Gaussian distributions, are over‐sensitive to rare events and heavy tailed behaviour, and can inappropriately indicate ‘more time‐more variation’ effects (reddened spectrum). Here I demonstrate that, while PV behaves similarly for ‘normal’ time series, it is independent of deviation from mean abundance for heavy tailed distributions, its robustness to non‐Gaussian behaviour resolves artificial reddened spectrum issues, and variability calculated using PV from short time series is substantially more accurate at estimating known long term variability than standard metrics. PV therefore provides common ground for evaluating the variability of populations undergoing different dynamics, and with different statistical distributions of abundance, and can be easily generalized to a variety of contexts and disciplines.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".