Superposed epoch analysis using time-normalization: A Python tool for statistical event analysis
Bibliographic record
Abstract
A superposed epoch analysis (SEA) is a simple, yet powerful statistical analysis technique, used to identify patterns in the temporal evolution of observed quantities relative to defined epochs. In some cases, the event duration and time between epochs (epoch length) can be highly variable. If the measured response scales with the event duration or epoch length, then the underlying temporal patterns can be suppressed when analyzed in absolute time. In this article, we describe an adaptation of the traditional SEA, where we apply time-normalization to each event and present a Python package sea_norm which implements the time-normalized SEA. Rather than defining a singular epoch time, a start, epoch, and end time are defined for each event, separating each event into two intervals. For every event, the duration of both intervals is normalized to a common time axis, essentially stretching or compressing each interval, such that each respective epoch interval is the same length for all events. This technique has the advantage of identifying temporal patterns not observed in a traditional SEA. Given a time series, a list of event start, epoch, and end times, and specified binning dimensions the Python package sea_norm returns a time-normalized SEA analysis of the time-series. This technique is widely applicable across the Space Physics field, where events have defined start and end times, and where the response to those events may scale proportionally with event length. We provide examples demonstrating how the SEA code works with one-dimensional and two-dimensional time series, and how users can specify their own statistics to use in the superposed analysis (e.g., percentiles).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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 teacher head, 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".