Transport and eruption of mantle xenoliths creates a lagging problem
Bibliographic record
Abstract
Abstract Mantle-derived xenoliths entrained by low viscosity magmas indicate transport through great thicknesses of orogenic (≲50-80 km) and cratonic ( $$\lesssim$$ ≲ 20 km) mantle lithosphere. The size and density of xenoliths dictate that, relative to the ascending magma, they continuously settle during transport. This creates a lag time between when the initial sampling magma reaches the Earth’s surface and the arrival of the xenoliths. The magnitude of this lag time depends on xenolith properties, sampling depth and the magma ascent velocity. Here, using settling calculations, we develop this lag time concept and show how eruption durations and volumes can impact the distribution, abundance, and properties (e.g., sample depths, size) of xenoliths. Lag times can account for heterogenous xenolith distributions within volcanic deposits, the potential biased sampling of the mantle lithosphere, and can constrain minimum eruption volumes required to transport and erupt the deepest sourced xenoliths such as those producing diamondiferous kimberlites.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".