Development and application of the Branched and Isoprenoid GDGT Machine learning Classification algorithm (BIGMaC) for paleoenvironmental reconstruction
Bibliographic record
Abstract
Glycerol dialkyl glycerol tetraethers (GDGTs), including both the archaeal isoprenoid GDGTs (isoGDGTs) and the bacterial branched GDGTs (brGDGTs), have been used in paleoclimate studies to reconstruct temperature in marine and terrestrial archives. However, GDGTs are present in many different types of environments, with relative abundances that strongly depend on the depositional setting. This suggests that GDGT distributions can be used more broadly to infer paleoenvironments in the geological past. In this study, we analyzed 1153 samples from a variety of modern sedimentary settings for both isoGDGT and brGDGTs. We used machine learning on the GDGT relative abundances from this dataset to relate the lipid distributions to the physical and chemical characteristics of the depositional settings. We observe a robust relationship between the depositional environment and the lipid distribution profiles of our samples. This dataset was used to train and test the Branched and Isoprenoid GDGT Machine learning Classification algorithm (BIGMaC), which identifies the environment a sample comes from based on the distribution of GDGTs with high accuracy. We tested the model on the sedimentary record from the Giraffe kimberlite pipe, an Eocene maar in subantarctic Canada, and found that the BIGMaC reconstruction agrees with independent stratigraphic information, provides new information about the paleoenvironment of this site, and helps improve paleotemperature reconstruction. In cases where paleoenvironments are unknown or are changing, BIGMaC can be applied in concert with other proxies to generate more refined paleoclimatic records.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".