Coastal ice-core record of recent northwest Greenland temperature and sea-ice concentration
Bibliographic record
Abstract
Abstract Coastal ice cores provide an opportunity to investigate regional climate and sea-ice variability in the past to complement hemispheric-scale climate reconstructions from ice-sheet-interior ice cores. Here we describe robust proxies of Baffin Bay temperature and sea-ice concentration from the coastal 2Barrel ice core collected in the Thule region of northwest Greenland. Over the 1990–2010 record, 2Barrel annually averaged methanesulfonic acid (MSA) concentrations are significantly correlated with May–June Baffin Bay sea-ice concentrations and summer temperatures. Higher MSA is observed during warmer years with less sea ice, indicative of enhanced primary productivity in Baffin Bay. Similarly, 2Barrel annually averaged deuterium excess (d-excess) values are significantly correlated with annual Baffin Bay sea-ice concentrations and summer and annual temperatures. Warm (cool) years with anomalously low (high) sea-ice concentration are associated with proportionally more (less) low-d-excess Baffin Bay moisture at the ice-core site. Multilinear regression models incorporating 2Barrel MSA, d-excess and snow accumulation account for 38–51% of the Baffin Bay sea-ice and temperature variance. The annual temperature model is significantly correlated with temperatures throughout most of Greenland and eastern Arctic Canada due to the strong influence of the North Atlantic Oscillation and Atlantic Multidecadal Oscillation.
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 | 0.000 | 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 | 0.000 | 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".