Macrofaunal burrowing enhances deep-sea carbonate lithification on the Southwest Indian Ridge
Bibliographic record
Abstract
Deep-sea carbonates represent an important type of sedimentary rock due to their effect on the composition of the upper oceanic crust and their contribution to deep-sea geochemical cycles. However, the role of deep-sea macrofauna in carbonate lithification remains poorly understood. A large lithified carbonate area, characterized by thriving benthic faunas and a tremendous amount of burrows, was discovered in 2008, blanketing the seafloor of the ultraslowly spreading Southwest Indian Ridge (SWIR). Benthic inhabitants – including echinoids, polychaetes, gastropods and crustaceans – are abundant in this carbonate lithified area. The burrowing features within these carbonate rocks, as well as the factors that may influence deep-sea carbonate lithification, were examined. We suggest that burrowing in these carbonate rocks enhances deep-sea carbonate lithification. We propose that active bioturbation may trigger the dissolution of the original calcite and thus accelerate deep-sea carbonate lithification on mid-ocean ridges. Macrofaunal burrowing provides a novel driving force for deep-sea carbonate lithification at the seafloor, illuminating the geological and biological importance of bioturbation in global deep-sea carbonate rocks.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".