Investigation of the Arctic Sea ice volume from 2002 to 2018 using multi‐source data
Bibliographic record
Abstract
Abstract The Arctic sea ice volume (SIV) was investigated by applying sea ice concentration (SIC) and multi‐source sea ice thickness (SIT) products from the Pan‐Arctic Ice‐Ocean Modelling and Assimilation System (PIOMAS), Envisat and CryoSat‐2 (CS‐2) products. The SIV was estimated during the sea ice growth season (October–April) from October 2002 to December 2018. During the Envisat period (October 2002–April 2010), negative SIV trends were estimated by a hybrid Envisat and PIOMAS SIT dataset (defined as Envi‐PIO); the declining trends for both the maximum/minimum SIV were 360 and 177 km3⋅year−1, respectively; similar SIV trends were obtained by applying only the PIOMAS SIT data. During the CS‐2 period (October 2010–December 2018), no clear trends in the SIV were estimated by either CS‐2 or PIOMAS, except for clear increases in the SIV in northern Greenland and the Canadian Arctic Archipelago (CAA) using the CS‐2 SIT data. The age of sea ice plays an important role in SIV variability. For example, the SIV trend was found to be similar to the multi‐year ice trend between 2003 and 2007. The correlation coefficients between the monthly mean SIV and surface air temperature (SAT) and sea surface temperature (SST) were −0.60 and −0.82, respectively. The decreasing trend in the SIV during the Envisat period was influenced by the increase in the annual maximum SST and minimum SAT. The significant increase in the SIV in northern Greenland and the CAA during the CS‐2 period was related to ice deformation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".