Alkenone unsaturation in surface sediments from the eastern equatorial Pacific: Implications for SST reconstructions
Bibliographic record
Abstract
Significant uncertainties persist in the reconstruction of past sea surface temperatures in the eastern equatorial Pacific, especially regarding the amplitude of the glacial cooling and the details of the post‐glacial warming. Here we present the first regional calibration of alkenone unsaturation in surface sediments versus mean annual sea surface temperatures (maSST). Based on 81 new and 48 previously published data points, it is shown that open ocean samples conform to established global regressions of U37K′ versus maSST and that there is no systematic bias from seasonality in the production or export of alkenones, or from surface ocean nutrient concentrations or salinity. The flattening of the regression at the highest maSSTs is found to be statistically insignificant. For the near‐coastal Peru upwelling zone between 11–15°S and 76–79°W, however, we corroborate earlier observations that U37K′ SST estimates significantly over‐estimate maSSTs at many sites. We posit that this is caused either by uncertainties in the determination of maSSTs in this highly dynamic environment, or by biasing of the alkenone paleothermometer toward El Niño events as postulated by Rein et al. (2005).
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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) | 0.000 | 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 | 0.000 |
| 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 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".