A Bayesian framework for inferring regional and global change from stratigraphic proxy records (StratMC v1.0)
Bibliographic record
Abstract
Abstract. The chemistry of ancient sedimentary rocks encodes information about past climate, element cycling, and biological innovations. Records of large-scale Earth system change are constructed by piecing together geochemical proxy data from many different stratigraphic sections, each of which may be incomplete, time-uncertain, biased by local processes, and diagenetically altered. Accurately reconstructing past Earth system change thus requires correctly correlating sections from different locations, distinguishing between global and local changes in proxy values, and converting stratigraphic height to absolute time. Incomplete consideration of the uncertainties associated with each of these challenging tasks can lead to biased and inaccurate estimates of the magnitude, duration, and rate of past Earth system change. Here, we address this shortcoming by developing a Bayesian statistical framework for inferring the common proxy signal recorded by multiple stratigraphic sections. Using the principle of stratigraphic superposition and both absolute and relative age constraints, the model simultaneously correlates all stratigraphic sections, builds an age model for each section, and untangles global and local signals for one or more proxies. Synthetic experiments confirm that the model can correctly recover proxy signals from incomplete, noisy, and biased stratigraphic observations. Future applications of the model to the geologic record will enable geoscientists to more accurately pose and test hypotheses for the drivers of past proxy perturbations, generating new insights into Earth's history. The model is available as an open-source Python package (StratMC), which provides a flexible and user-friendly framework for studying different times and proxies recorded in sediments.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".