Multifractal patterns in the daily catch time series of smooth pink shrimp (<i>Pandalus jordani</i>) from the west coast of Vancouver Island, Canada
Bibliographic record
Abstract
There is an urgent need for indicators that anticipate changes in populations of exploited marine species. We modelled the complex patterns of variability found in fisheries time series that are not detected using classical models. We applied fractal analyses to detect time-invariant scaling symmetries in daily catch time series from the smooth pink shrimp ( Pandalus jordani ) fishery from the west coast of Vancouver Island, British Columbia, Canada. A universal multifractal model, which accounts for intermittent fluctuations and extreme values in a time series, provided a better fit to daily catches than a monofractal model. Multifractality is an indication of multiple scaling patterns and suggests that more than one process is affecting the variability of catches. Fractal dynamics in catch time series were found for a range of scales between 16 and 120 fishing days. To our knowledge, this is the first time that multifractality has been demonstrated for an invertebrate fishery. The multifractal model has the potential to provide an in-season estimate (up to 120 fishing days) of the variability of shrimp catches based on the variability at time scales less than 1 month. Changes in these fractal patterns may provide an early warning that conditions underlying this fishery are changing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".