The spreading-rate dependence of anomalous skewness of Pacific plate magnetic anomaly 32: Revisited
Bibliographic record
Abstract
We test the consistency of 108 estimates of skewness (a measure of asymmetry that depends on the orientation of lithospheric magnetization) of magnetic anomaly 32 from the Pacific plate with a model for spreading-rate–dependent anomalous skewness formulated for data in the Arctic, Atlantic, and Indian Oceans. In a prior study, a chron 32 (71.6–73.0 Ma) paleomagnetic pole was determined that best fit these 108 skewness estimates while simultaneously solving for a third parameter, anomalous skewness, assumed to be independent of spreading rate. An analysis of the residuals in skewness was previously used to test for any dependence on spreading rate and indicated an increase in residual skewness with increasing spreading rate, which is opposite in trend to that observed in other ocean basins. In contrast with the prior analysis of residuals, we find the data to be consistent with anomalous skewness increasing with decreasing spreading half rate less than 50 mm yr−1. Thus, the spreading-rate dependence of anomalous skewness in the Pacific is consistent with that found in other ocean basins and with the model for spreading-rate–dependent anomalous skewness. The resulting revised paleomagnetic pole lies only 1.2° from the prior pole. The revised pole, as was the case for the original pole, shows that the Hawaiian hotspot has shifted southward relative to the spin axis by 13° since ca. 72 Ma.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".