Endolithic Boring Enhance the Deep-sea Carbonate Lithification on the Southwest Indian Ridge
Bibliographic record
Abstract
Abstract. Deep-sea carbonates represent an important type of sedimentary rock due to their effect on the composition of upper oceanic crust and their contribution to deep-sea geochemical cycles. However, the lithification of deep-sea carbonates at the seafloor has remained a mystery for many years. A large lithified carbonate area, characterized by thriving benthic faunas and tremendous amount of endolithic borings, was discovered in 2008, blanketed on the seafloor of ultraslow spreading Southwest Indian Ridge (SWIR). Macrofaunal inhabitants including echinoids, polychaetes, gastropods as well as crustaceans, are abundant in the sample. The most readily apparent feature of the sample is the localized enhancement of density around the borings. The boring features of these carbonate rocks and factors that may enhance deep-sea carbonate lithification are reported. We suggest that active boring may trigger the dissolution of the original calcite and thus accelerate deep-sea carbonate lithification on mid-ocean ridges. Our study reports an unfamiliar phenomenon of non-burial carbonate lithification and interested by the observation that it is often associated with boring feature. These carbonate rocks may provide a novel mechanism for deep-sea carbonate lithification at the deep-sea seafloor and also illuminate the geological and biological importance of deep-sea carbonate rocks on mid-ocean ridges.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".