Production and Preservation of Lipid Biosignatures in SO4-Rich Hypersaline Lakes of the Cariboo Plateau
Bibliographic record
Abstract
Modern and ancient hypersaline lakes and oceans have been identified across the solar system, but the habitability and potential of these environments to preserve organic matter remain unknown. Here, we evaluate organic matter production and preservation potential in hypersaline lakes whose chemistries resemble deposits on Mars. We focus our analysis on lipid biomarkers including fatty acids, alkanes, and ether-bound lipids in modern brines, salt deposits, and surface sediments. We also report total organic carbon (TOC), carbon/nitrogen (C/N) ratios, and bulk OC (δ13C and δ15N) isotopes to contextualize the lipid data. In all lakes, the predominant biosignatures include short chain fatty acids (C<23) suggesting microbial origin. Sediments also incorporate a diversity of microbially and terrestrially derived lipids. Ether-bound lipids derived from archaea and bacteria constitute a minor but measurable fraction of the lipids in brines. This result contrasts with typical results from NaCl brines which contain significant archaeal biomass. TOC concentrations in sediments are universally high, ranging from 0.7% to 12% with sulfate-rich sediments having the highest concentrations. The isotopic composition of TOC corroborates the biomarker results, showing δ13C values and C/N values indicative of aquatic microbial origin. This richness of organic material and in situ microbial biosignatures differ from previously studied Cl-dominated Mars-analog sites which have shown limited organic matter production and preservation and acidic SO4-rich hypersaline environments which were dominated by terrestrial inputs. Overall, our results suggest that Mg-SO4-rich hypersaline environments harbor a rich microbial biomarker landscape and are ideal locations for preserving these signatures, potentially over geological timescales.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".