A Global Data Set of Present‐Day Oceanic Crustal Age and Seafloor Spreading Parameters
Bibliographic record
Abstract
Abstract We present an updated oceanic crustal age grid and a set of complementary grids including spreading rate, asymmetry, direction, and obliquity. Our data set is based on a selected set of magnetic anomaly identifications and the plate tectonic model of Müller et al. (2019, 10.1029/2018TC005462 ). We find the mean age of oceanic crust is 64.2 Myr, slightly older than previous estimates, mainly due to the inclusion of pockets of Mesozoic aged crust in the Atlantic and Mediterranean and improvements to the Jurassic Pacific triangle. This older crust is partly compensated by additional Cenozoic‐aged back‐arc basin crust not included in previous models. The distribution of spreading modes based on area of preserved crust is relatively equal between slow (20–55 mm/yr) and fast (75–180 mm/yr) spreading systems at 33% and 39%, respectively. Crust transitional between fast and slow, or intermediate systems (55–75 mm/yr), cover 20% of the preserved ocean floor with much smaller proportions of crust formed at ultraslow (5%) and super‐fast (3%) spreading systems. Slow and intermediate spreading systems exhibit the most stable behavior in terms of spreading asymmetry and obliquity, with the widest distribution of obliquities occurring at ultraslow spreading systems, consistent with present‐day observations. Our confidence grid provides a complementary resource for nonexperts to identify those parts of the age grid that are least well constrained. Our grids in 6, 2, and 1 arc min resolution as well as our python workflow, isopolate , used to compute our data sets are freely available in online repositories and on the GPlates data portal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".