Sea-ice coverage variability on the Northern Sea Routes, 1980–2011
Bibliographic record
Abstract
Abstract We analyze sea-ice conditions along seven segments of the Northern Sea Route (NSR) over four time periods. We researched sea ice by segment, using data from the satellite microwave sensors SMMR, SSM/I and AMSR-E. The four analysis periods (periods I–IV: 1980–88, 1989–2001, 2002–06 and 2007–11, respectively) were determined based on changes in the extent of minimum sea ice throughout the Arctic Ocean. Sea ice showed a decreasing tendency from period I to period IV. For example, sea-ice area in period IV decreased compared to previous periods in the eastern East Siberian Sea and around Severnaya Zemlya, areas that had very high sea-ice concentrations in period I. Sea-ice area in the eastern East Siberian Sea decreased sharply during period III, whereas the Severnaya Zemlya area maintained a high ice concentration. In period IV, sea-ice coverage around Severnaya Zemlya was low, although it remained at 25% in the area east of Severnaya Zemlya, which is a key area for navigation. The proportion of multi-year (MY) ice drastically decreased after winter 2002, and only a small amount of MY ice existed in the winters of 2003–06. MY ice disappeared from the eastern East Siberian Sea after 2007. On the other hand, around Severnaya Zemlya the proportion of MY ice showed cyclic ups and downs between 1997 and 2008. Thus, the persistence of various types of sea ice varies according to region. The persistence of various types of sea ice around Severnaya Zemlya also varied each year.
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".