Alkenone paleothermometry in the North Atlantic: A review and synthesis of surface sediment data and calibrations
Bibliographic record
Abstract
Abstract Despite a clear correlation of alkenone unsaturation and sea surface temperatures (SST) throughout most parts of the ocean, scatter of the regression for various calibration equations has been shown to increase significantly at low SSTs. In this study, we combine previously published ( n = 101) and new ( n = 51) surface sediment data from the northern North Atlantic to constrain uncertainties of alkenone paleothermometry at low SSTs and to discuss possible sources of the increased scatter in the regression. The correlation between alkenone unsaturation and SSTs is strongest, in particular at the cold end (SSTs < 10°C), when the tetra‐unsaturated alkenones (C 37:4 ) are included in the unsaturation index (expressed as ) and regressed against spring‐summer temperature. Surface ocean salinity and sea ice cover are not correlated with per se. However, samples located in regions of permanent winter sea ice cover exhibit a significant warm bias. Deviation from the linear regression is posited to be related to a number of additional non‐exclusive factors, such as advection of allochthonous material, local temperature stratification, and uncertainty in the absolute age of surface sediment samples assumed to be equivalent to modern conditions. We conclude that alkenone unsaturation allows accurate reconstruction of SST records from many regions of the North Atlantic if the factors confounding alkenone paleothermometry detailed here can be excluded.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".