Trace metal elements as paleoenvironmental proxies: Why should we account for sedimentation rate variations?
Bibliographic record
Abstract
Abstract Trace metal elements (TMEs) are commonly used to reconstruct the environmental conditions present during the deposition of organic-rich sediments. For example, TME concentrations controlled by changes in primary productivity and redox conditions are widely used in paleoenvironmental studies. Recently, these proxies have undergone a resurgence of interest and are commonly used in large-scale (10–1000 km) studies. However, applying these geochemical proxies at basin scale while ignoring variations in sedimentation rates (SR) may lead to misinterpretation of paleoenvironmental conditions. Here, we show how SR can affect the geochemical records and may lead to incorrect interpretations of TME evolution. Accounting for SR, we computed the authigenic fraction accumulation rates of key TMEs in the Upper Montney Formation and Doig Phosphate (Triassic, western Canada), and we correct the concentration of these elements in the Vaca-Muerta Formation (Jurassic–Cretaceous, Argentina). Our SR-corrected TME proxies require a different interpretation of paleoenvironmental conditions (e.g., primary productivity, basin restriction) compared to conventional TME results and highlight that elementary enrichments commonly interpreted as indicative of anoxic depositional environments may reflect low SR and the formation of condensed intervals. This work also introduces a new workflow to account for SR in paleoenvironmental studies at basin scale and over long time periods.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".