Coralline Algae Archive Fjord Surface Water Temperatures in Southwest Greenland
Bibliographic record
Abstract
Abstract One of the most dramatic signs of ongoing global change is the mass loss of the Greenland Ice Sheet and the resulting rise in sea level, whereby most of the recent ice sheet mass loss can be attributed to an increase in meltwater runoff. The retreat and thinning of Greenland glaciers has been caused by rising air and ocean temperatures over the past decades. Despite the global scale impact of the changing ice sheet balance, estimates of glacial runoff in Greenland rarely extend past several decades, thus limiting our understanding of long‐term glacial response to temperature. Here we present a 42‐year long annually resolved red coralline algal Mg/Ca proxy temperature record from a southwestern Greenland fjord, with temperature ranging from 1.5 to 4 °C (standard error = 1.06 °C). This temperature time series in turn tracks the general trend of glacial runoff from four West Greenland glaciers discharging freshwater into the fjord (all p < 0.001). The algal time series further exhibits significant correlations to Irminger Sea temperature patterns, which are transmitted to western Greenland fjords via the West Greenland Current. The 42‐year long record demonstrates the potential of annual increment forming coralline algae, which are known to live up to 650 years and which are abundant along the Greenland coastline, for reconstructing time series of sea surface temperature.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".