Quantifying crustal growth in the Lau arc-backarc system through gravity inverse modelling
Bibliographic record
Abstract
The formation and evolution of arc-backarc systems govern crustal production in some of the most volcanically and hydrothermally active environments on Earth. Geologic mapping of these systems is increasingly possible by interpretation of emerging ship-based and global geophysical datasets. Although specific rock types cannot be confidently identified from a single physical property, the relative density changes across a region can provide information about the composition of the crust and how it was formed, for example, indicating whether old crust was produced along the volcanic arc or at a back-arc spreading center. This study presents the first complete three-dimensional density model of the Lau Basin and Tofua arc-backarc system in the southwest Pacific Ocean. Seafloor density and crustal thickness maps were produced that reveal changes in crustal composition and growth rates throughout the basin and along the volcanic arc. Crustal thickness varies greatly between the different centers of accretion (indicated here by assemblages), reflecting seafloor spreading and subsurface melt accumulation below volcanic fields. Volumetric growth rates were calculated for each assemblage, corresponding to their respective contribution to basin expansion. The highest crustal density and growth rates are thought to be related to a mantle-derived melt source entering the basin from the north around the edge of the subducting Pacific Plate. The inverse modelling approach used in this study can be applied to global gravity datasets to characterize and quantify the density and thickness of the crust anywhere in the oceans.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".