Determining the controls on faecal stanol concentrations and ratios in tropical lake sediments
Bibliographic record
Abstract
Faecal stanols offer an exciting opportunity to determine population change in the past but the controls of their concentrations and ratios within lake sediments are not well understood. We present the variability in stanol concentrations and ratios from lakes across environmental gradients, both between and in lakes across climatic and land-use gradients in Mexico, Guatemala, and Belize in order to determine the factors controlling preservation and degradation in lacustrine sediments. We also measured physical properties of lakes such as dissolved oxygen, pH, and water column temperature and estimated the approximate human population in each catchment, with the goal of producing a semi-quantitative calibration of human population to coprostanol+epi-coprostanol as a ratio to cholestanol, a more widely produced bacterial stanol. In particular we explore the hypothesis that a dominant control on concentrations and ratios is proximity to a human settlement. We evaluate this hypothesis in two lakes (L. Peten-Itza and L. Izabal) where we collected samples at varying distances from major population centres. This will have implications for the targeting of lake cores in studies where determining population change is the goal. In addition to this work we will share three intriguing preliminary palaeo-records of stanol concentrations from Guatemala (Laguna Itzan, Laguna Peten-Itza, and Lago Izabal). These records imply highly dynamic millennial scale changes in human populations, and we apply the modern sediment data to better constrain the interpretation of these data. Our work shows that faecal stanols have a strong potential as proxies for changes in human population and land-use change through time, and can be used to complement archaeological datasets to link human populations with palaeoenvironmental change.
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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 | 0.001 |
| 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 | 0.001 |
| 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".