Tracking the Pacific Decadal Precession
Bibliographic record
Abstract
Abstract Events of recent years—including extended droughts across California, record fires across western Canada, and destabilization of marine ecosystems—highlight the profound impact of multiannual to decadal‐scale climate shifts upon physical, biological, and socioeconomic systems. While previous research has focused on the influence of decadal‐scale climate oscillations such as the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation/Interdecadal Pacific Oscillation, recent research has revealed the presence of a quasi‐decadal mode of climate variability that, unlike the quasi‐stationary standing wave‐like structure of the oscillatory modes, involves a progression of atmospheric pressure anomalies around the North Pacific, which has been termed the Pacific Decadal Precession (PDP). In this paper we develop a set of methods to track the spatial and temporal evolutions of the PDP within historical observations as well as numerical model simulations. In addition, we provide a method that approximates the time evolution of the PDP across the full period of available data for real‐time monitoring of the PDP. Through the development of these tracking methods, we hope to provide the community with a consistent framework for future analysis and diagnosis of the PDP's characteristics and underlying processes, thereby avoiding the use of different, and disparate, phenomenological‐ and mathematical‐based indices that can confound our understanding of the PDP and its evolution.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".