Bibliographic record
Abstract
Cyclic population dynamics are of central interest in ecology. Reliably identifying and quantifying the cyclicity of populations is valuable for the understanding of regulatory mechanisms and their variability across spatiotemporal scales. Cyclicity can be detected using periodogram analysis of time series. The statistical significance of periodogram peaks is commonly evaluated against the null hypothesis of uncorrelated fluctuations, also known as white noise. Here, we show that this null hypothesis is inadequate for cycle detection in ecosystems with non‐negligible correlation times. As an alternative null hypothesis we propose the so‐called Ornstein‐Uhlenbeck state‐space (OUSS) model, which generalizes white noise to allow for temporal correlations. We justify its use on mechanistic principles and demonstrate its advantages using numerical simulations of simple population models. We show that merely contrasting cyclicity against white noise greatly increases the false cycle detection rate and can lead to wrong conclusions even for simple systems. A comparative statistical analysis of the Global Population Dynamics Database using both null hypotheses suggests that a significant number of populations might have been misinterpreted as cyclic in the past. Our proposed methods for cycle detection are available as an R package (peacots).
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".