Reconstruction of recent sea-level change using testate amoebae
Bibliographic record
Abstract
Proxy-based sea-level reconstructions place the instrumentally observed rates of recent sea-level rise in a longer term context by providing data that extend the instrumental sea-level record into past centuries. This paper presents the first sea-level reconstructions based on analyses of testate amoebae, to test their ability to produce high-precision reconstructions of past sea level. We present two reconstructions for the past 100"yr from sites in Maine (USA) and Nova Scotia (Canada) based on short cores from salt marshes, and modern training data from North America and the United Kingdom. These are compared with tide-gauge records and reconstructions based on foraminifera from the same cores. The reconstructions show good agreement with both the tide-gauge data and the foraminifera-based reconstructions. The UK data perform well in predicting known elevations of North American surface samples and produce sea-level reconstructions very similar to those based on the North American data, suggesting the methodology is robust across large geographical areas. We conclude that testate amoebae have the potential to provide robust, higher precision sea-level reconstructions for the past few centuries if modern transfer functions are improved and core sites are located within the main zone of testate amoebae occurrence on the salt marsh.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".