A Meteoric Water Budget for the Arctic Ocean
Bibliographic record
Abstract
Abstract A budget of meteoric water (MW = river runoff, net precipitation minus evaporation, and glacial meltwater) over four regions of the Arctic Ocean is constructed using a simple box model, regional precipitation‐evaporation estimates from reanalysis data sets, and estimates of import and export fluxes derived from the literature with a focus on the 2003–2008 period. The budget indicates an approximate/slightly positive balance between MW imports and exports (i.e., no change in storage); thus, the observed total freshwater increase observed during this time period likely resulted primarily from changes in non‐MW freshwater components (i.e., increases in sea ice melt or Pacific water and/or a decrease in ice export). Further, our analysis indicates that the MW increase observed in the Canada Basin resulted from a spatial redistribution of MW over the Arctic Ocean. Mean residence times for MW were estimated for the Western Arctic (5–7 years), Eastern Arctic (3–4 years), and Lincoln Sea (1–2 years). The MW content over the Siberian shelves was estimated (∼14,000 km3) based on a residence time of 3.5 years. The MW content over the entire Arctic Ocean was estimated to be ≥44,000 km3. The MW export through Fram Strait consisted mostly of water from the Eastern Arctic (3,237 ± 1,370 km3 yr−1) whereas the export through the Canadian Archipelago was nearly equally derived from both the Western Arctic (1,182 ± 534 km3 yr−1) and Lincoln Sea (972 ± 391 km3 yr−1).
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".