It is not just about the ice: a geochemical perspective on the changing Arctic Ocean
Bibliographic record
Abstract
Much concern has accompanied the dramatic decrease in area covered by permanent pack ice in the Arctic Ocean during the past two decades. Ice is undeniably the most obvious feature distinguishing the Arctic Ocean, and its loss seizes public and scientific attention like no other tipping point. Beneath that challenging ice surface lies an ocean that is strongly affected by other less-visible factors that also have a large say in how change will occur in this ocean. Especially important to the Arctic Ocean is its connection to the surrounding land, which feeds it fresh water and organic carbon, and the large shelves and enclosed geography that accentuate the importance of these external factors. Like the sea ice, land is changing rapidly due to widespread thawing of permafrost. For the three global risks that have been deeply thought about recently in the context of Arctic Ocean ecosystems (i.e. contaminants, warming, ocean acidification), the Arctic appears to be exceptionally sensitive, sufficiently so that it has been termed a bellwether for each. Here, we examine how the less-visible factors (fresh water, organic carbon cycling) affect the Arctic’s reception of risk and its potential to export risk to the rest of the globe. We conclude that there needs to be a better coordinated effort to collect time series for the terrestrial components cycling within the Arctic Ocean such that we can understand what is happening to the marine components.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".