Late twentieth century increase in northern Spitsbergen (Svalbard) glacier-derived runoff tracked by coralline algal Ba/Ca ratios
Bibliographic record
Abstract
Abstract The Arctic cryosphere is changing rapidly due to global warming. Northern Svalbard is a warming hotspot with a temperature rise of ~ 6 °C over the last three decades. Concurrently, modelled data suggest a marked increase in glacier runoff during recent decades in northern Svalbard, and runoff is projected to increase. However, observational data from before anthropogenic influence are sparse and the potential effects on the surface ocean are unclear. Here, we present a 200-year record of Ba/Ca ratios measured in annual increment-forming coralline algae from northern Spitsbergen as a proxy for past glacier-derived meltwater input. Our record shows a significant increasing trend in algal Ba/Ca ratios from the late-1980s onwards matching modelled regional runoff data, suggesting a drastic increase in land-based runoff. The rate of increase is unprecedented during the last two centuries and captures the impact of amplified warming on the coastal surface ocean in the high Arctic. The algal Ba/Ca runoff proxy offers an opportunity to reconstruct past land-based runoff variability in Arctic settings in high resolution, providing important data for validating and improving climate modelling studies.
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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".