A multi-proxy modern training set for reconstructing Holocene relative sea level using salt-marsh sediment (Prince Edward Island, Canada)
Bibliographic record
Abstract
Reconstructing Holocene relative sea-level change from salt-marsh sediment requires a modern training set that captures the observable relationship between proxies and local tidal elevation. We collected 143 surface sediment samples from four salt marshes in Prince Edward Island (PEI, Canada) to develop a modern training set of foraminifera and bulk-sediment δ13C and δ15N values. Two sites have semi-diurnal tidal regimes, one has a mixed regime, and one has a diurnal regime. Combining and standardizing datasets from different tidal regimes fails to adequately characterize the relationship between elevation and inundation. Our principal results are from the semi-diurnal sites. Salt-marsh foraminifera in PEI form low-diversity, elevation-dependent zones that are replicated within sites, among sites, and between two studies conducted 44 years apart. Botanical zonation of PEI salt marshes often includes a platform with C4 plants and an adjacent, higher transitional community of C3 plants. Bulk-sediment δ13C values mirror the dominant vegetation. Isotopic results from PEI are combined with datasets from Delaware to Massachusetts to explore how elevation thresholds and δ13C cut-off values can recognize environments of deposition. We propose that δ13C values more negative than –20.0‰ indicate formation above an elevation threshold placed slightly below mean higher high water, while δ13C values less negative than –17.0‰ characterize environments lower than an elevation threshold slightly above mean higher high water. Bulk-sediment δ15N values display a correlation with tidal elevation and botanical zone, but may be subject to anthropogenic and post-depositional modification limiting their utility as a sea-level proxy. The dataset of modern foraminifera is suitable for reconstructing relative sea level and could be combined with informative δ13C values in a multi-proxy approach.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".