Geochemical Identification of Clastic Sediment Provenance from Known Sources of Similar Geology: The Cretaceous Scotian Basin, Canada
Bibliographic record
Abstract
Abstract This study tests the effectiveness of a geochemical approach in identifying provenance in a basin where different sources do not show strongly contrasting geology. Petrological studies indicate that at least three distinct rivers, draining reactivated horsts of the Appalachian orogen, supplied sediment to the Lower Cretaceous deltaic sandstones and mudrocks in the offshore Scotian Basin. Ninety-five samples mostly from conventional core were analyzed for 44 major and trace elements. The data were first screened for variability unrelated to provenance, including changes in elemental abundance due to weathering and diagenesis, and the effects of grain size and sorting on element variation. The effect of hydraulic sorting was distinguished from the effects of concentration of ultrastable heavy minerals from polycyclic sources. Multivariate statistical analysis by principal-component analysis (PCA) was used to test the hypothesis of three discrete sources suggested by the petrologic model. Major-element PCA on sandstones discriminated the sources, but results from trace-element PCA required further investigation using element biplots to understand their significance. Only a few elements were found to be diagnostic of different sources, namely K, Rb, Sr, U, Th, Nb, and Ti, with the latter two of value only for sandstones. All of these are elements that are abundant in the granites of the Appalachians. Most published geochemical discrimination diagrams did a poor job of distinguishing the three petrographically recognized sources. In other basins with quite different hinterland geology, other elements are known to be of value in discriminating provenance. Thus it is unlikely that a globally applicable set of elemental discriminants can identify terrigenous sediment sources. Rather, systematic investigation is needed that evaluates processes such as diagenesis and sorting and then tests geographic and stratigraphic variability in bulk geochemistry, informed by at least semiquantitative petrographic data.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".