Brief communication: An alternative method for estimating the scavenging efficiency of black carbon by meltwater over sea ice
Bibliographic record
Abstract
The meltwater scavenging coefficient (MSC) of black carbon (BC) is a crucial parameter in snow and sea ice models, as it determines the BC enrichment in the surface layer of melting snow over sea ice and therefore modulates the BC–snow–albedo feedbacks. We present a new method for MSC estimation by sampling the melt–refreeze ice layer that is produced from refreezing of the meltwater within snowpack and its overlying snow and measuring their physical characteristics in Elson Lagoon northeast of Utqiaġvik (formerly Barrow), Alaska, during the melting season. The bias of estimated MSC ranges from −5.4 % to 7.3 %, which is not exactly dependent on the degree of ablation. The average MSC value calculated with this proposed method is slightly lower than that derived from the repeating sampling (RS) method in Elson Lagoon while still being within its best estimate range. Further estimation demonstrates that the MSC in the Canada Basin (23.6 % ±2.1 % ) is close to that in Greenland (23.0 % ±12.5 % ) and larger than that in the Chukchi Sea (17.9 % ±5.0 % ) in the northwest of Utqiaġvik. Elson Lagoon has the lowest MSC (14.5 % ±2.6 % ) in the study areas. The method suggested in this study provides a possible approach for large-scale measurements of MSC over the sea ice area in the Arctic. Of course, this method depends on the presence of a melt–refreeze ice layer in the observation area.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".