Biomarker Distributions in (Sub)‐Arctic Surface Sediments and Their Potential for Sea Ice Reconstructions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract To evaluate the present sea ice changes in a longer‐term perspective, the knowledge of sea ice variability on preindustrial and geological time scales is essential. For the interpretation of proxy reconstructions it is necessary to understand the recent signals of different sea ice proxies from various regions. We present 260 new sediment surface samples collected in the (sub‐)Arctic Oceans that were analyzed for specific sea ice (IP 25 ) and open‐water phytoplankton biomarkers (brassicasterol, dinosterol, and highly branched isoprenoid [HBI] III). This new biomarker data set was combined with 615 previously published biomarker surface samples into a pan‐Arctic database. The resulting pan‐Arctic biomarker and sea ice index (PIP 25 ) database shows a spatial distribution correlating well with the diverse modern sea ice concentrations. We find correlations of P B IP 25 , P D IP 25 , and P III IP 25 with spring and autumn sea ice concentrations. Similar correlations with modern sea ice concentrations are observed in Baffin Bay. However, the correlations of the PIP 25 indices with modern sea ice concentrations differ in Fram Strait from those of the (sub‐)Arctic data set, which is likely caused by region‐specific differences in sea ice variability, nutrient availability, and other environmental conditions. The extended (sea ice) biomarker database strengthens the validity of biomarker sea ice reconstructions in different Arctic regions and shows how different sea ice proxies combined may resolve specific seasonal sea ice conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it