Limitations in correlation of regional relative geomagnetic paleointensity
Bibliographic record
Abstract
Time domain correlations of common features among relative paleointensity records from sedimentary cores are invaluable to paleomagnetism and paleoclimatology. Sediments with high accumulation rates might now provide millennial scale correlations of temporal variations in the geomagnetic dipole moment. Errors in the ages of paleomagnetic data samples, however, can make such correlations difficult and unreliable. We use spectral methods to assess the level of coherence expected among individual and stacked high‐resolution simulated paleointensity records for the time interval 0–75 ka. Correlations between individual paleointensity records are systematically degraded with decreased sedimentation rate and increased magnitude of age errors. We find that with optimistic age errors and interpolation of depth sampled data to evenly spaced time series, only short period signal in high‐resolution relative paleointensity is corrupted. For currently available methods of establishing chronologies, we estimate the minimum characteristic timescale of correlative features between pairs of regional stacked records at about 4.5 kyr. From an analysis of NAPIS‐75 and SAPIS data, it appears that the limit is inherent to the regional stacks and not a consequence of comparison of distant, independent data sets. A detailed comparison of the NAPIS‐75 and SAPIS stacks shows that this limit is likely larger, perhaps 6 kyr. At long periods the two regional stacks are more poorly correlated than those from our simulations, suggesting somewhat larger age errors in the individual paleointensity records.
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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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".