The Role of Uncertainty in Estimating Lead/Lag Relationships in Marine Sedimentary Archives: A Case Study From the Tropical Pacific
Bibliographic record
Abstract
Abstract Understanding the mechanisms behind any changes in the climate system often requires establishing the timing of events imprinted on the geological record. However, these proxy records are prone to large uncertainties, which may preclude meaningful conclusions about the relative timing of events. In this study, we put forth a framework to estimate the uncertainty in phase relationships inferred from marine sedimentary records. The novelty of our method lies in the accounting of the various sources of uncertainty inherent to paleoclimate reconstruction and timing analysis. Specifically, we use a Monte Carlo process allowing sampling of possible realizations of the time series as functions of uncertainties in time, the climate proxy, and the identification of the termination timing. We then apply this technique to 15 published sea surface temperature records from the equatorial Pacific to evaluate whether we observed any significant changes in the termination timing between the east and the west. We find that the uncertainty on the relative timing estimates is on the order of several thousand years and mainly stems from age model uncertainty (90%). However, even small differences in mean termination timings can be detected with a sufficiently large number of samples. Improvements in the dating of sediment records provide an opportunity to reduce uncertainty in studies of this kind.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".