Acceleration of sea-ice melting due to transmission of solar radiation through ponded ice area in the Arctic Ocean: results of in situ observations from icebreakers in 2006 and 2007
Bibliographic record
Abstract
Abstract Sea-ice melting processes were inferred from in situ sea-ice and ocean condition data obtained in the Arctic in summer 2006 and 2007. the relationship between ice concentration observed by on-board ice watches and water temperature showed negative correlations. This implies that as ice concentration decreases, the upper ocean becomes warmer due to greater absorption of solar radiation into open water, which promotes ice melting. However, heating of surface water is significant even in regions that were almost completely ice-covered, suggesting that transmitted solar radiation through the ice is also effective at melting sea ice. A simplified ice–upper-ocean coupled model was applied to examine the effect of heat input from open water, thick ice and thin ice. the ponded thin ice is estimated to transmit approximately three times more solar radiation than ponded thick ice. Model results suggest that transmission of solar radiation through ponded ice amplified the ice-albedo feedback mechanism, particularly in thin ice regions. Recently, the extent of old and thick multi-year ice in the Arctic Ocean has been rapidly reduced. As a result, heat input to the upper ocean through the ice is enhanced and ice melt is further accelerated.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".