Elemental Geochemistry of Tidal Marsh Sediment and Its Potential as an Indicator of Sea‐Level Change in Cascadia
Bibliographic record
Abstract
Abstract Holocene sea‐level reconstructions from tidal marshes are commonly derived from proxy indicators that have a consistent and quantifiable relationship with tidal elevation. While microfossils are most commonly employed, using multiple indicators leads to more robust reconstructions. We explore the utility of elemental geochemistry obtained through x‐ray fluorescence as a proxy indicator in tidal marshes at Port Alberni, British Columbia, Canada and Willapa Bay, Washington, United States. The elemental composition of bulk surface sediment collected from 141 stations along 10 transects was determined using an ITRAX Core Scanner. Partitioning Around Medoids cluster analysis on the elemental data distinguished between tidal flat, low marsh, and high marsh zones at both locations, similar to zones established from previously published microfossil (foraminifera, diatoms) data sets on the same samples. The elemental composition of low elevation samples from the tidal flat is dominated by lithogenic (Si, K, Ti, Fe) and biogenic (Sr) elements, whereas higher elevation samples have high proportions of organic content (Br, incoherent and coherent scattering ratio). Principal Component Analysis points to differences in organic versus inorganic content, a function of tidal elevation, as the main driver of geochemistry‐derived zones. Approximately 70% of the elemental variability within both marshes is controlled by the inorganic content, as indicated by lithogenic and biogenic elements versus organic content. The elemental composition of bulk surface sediment from two regions spaced ∼300 km apart shows a promising relationship with tidal elevation over a wider spatial scale and highlights the potential of this proxy for use in sea‐level reconstructions.
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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 | 0.001 | 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".