Comment on cp-2021-99
Bibliographic record
Abstract
Greenland ice cores provide information about past climate. However, the number of firn and ice cores from Greenland are limited and thus the spatial variability of the chemical impurities used as proxies is largely unconstrained. Furthermore, few impurity records covering the past two decades exist from Greenland. We have by means of Continuous Flow analysis investigated 6 shallow firn cores obtained in Northern Greenland as part of the NEEM to EastGRIP traverse in 2015. The oldest reach back to 1966. The annual mean and quartiles of the insoluble dust, ammonium, and calcium concentrations in the 6 firn cores spanning a distance of 426 km overlap, and also the seasonal cycles have similar peaks in timing and magnitude across sites. Peroxide (H2O2) is accumulation dependent and varies from site to site and conductivity, likely influenced by sea salts, also vary spatially. The temporal variability of the records is further assessed. We find no evidence for increases in total dust concentration, but find an increase in the large dust particle fluxes that we contribute to an activation of Greenland local sources in the recent years (1998–2015). We observe the expected acid and conductivity increase in the mid 70’s as a result of anthropogenic contamination and the following decrease due to mitigation. After detrending using the five year average the conductivity and acid records several volcanic horizons were identified and associated with Icelandic eruptions and volcanic eruptions in the Barents sea region. By creating a composite based on excess ammonium compared to the five year running average, we obtain a robust forest fire proxy associated primarily with Canadian forest fires (R = 0.51). We also note that the peak ammonium in the individual firn cores appear more scattered between cores than the peak volcanic layers, suggesting that the forest fire signal is more dispersed in the atmosphere than the acid from volcanic eruptions.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.043 | 0.026 |
| Insufficient payload (model declined to judge) | 0.044 | 0.052 |
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".