Linking decay of microbial mats and dolomite formation in the sabkhas of Qatar
Bibliographic record
Abstract
The sabkhas of Qatar are excellent environments to examine the mechanisms of low-temperature dolomite precipitation. The detailed microbial and geochemical analysis of the dynamics in environmental conditions in two microbial mats over two years provide a unique opportunity to gain insights in low-temperature dolomite formation in modern time. The compositions of extracted exopolymeric substances (EPS) in two microbial mats, one within the lower intertidal zone and one within the upper intertidal zone exhibit an increase in the concentration of carboxylic functional groups during periods of elevated salinity. We interpret it as an indicator for dolomite formation since carboxylic functional groups are suggested to be the primary drivers for low-temperature dolomite as nucleation sites and inhibitors of Mg complexes. Notably, the increase in the concentration of the carboxylic group is associated with an increase in salinity in sabkha which happened periodically. These fluctuations have been accompanied by the changes in the community from cyanobacterial dominated mat to one dominated by heterotrophs. During these periodical events, when a growing microbial mat turned into degrading microbial mat, we observed low-temperature dolomite formation. Such events occur in other modern dolomite forming environments and possibly in ancient sequences. Our work observed dynamical changes both in microbial mats, exopolymeric substances composition, geochemical gradients and accompanied low-temperature dolomite formation over several seasons. Our findings proving evidence that EPS degradation within microbial mats is a key mechanism in the formation of modern and most probable, ancient low-temperature dolomite with implications for those formed in ancient sequences.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".