Bibliographic record
Abstract
Overview One of the keys to understanding the Arctic climate system is the determination of freshwater transfers. As introduced in Chapter 2, the Arctic Ocean is characterized by a relatively fresh surface layer, primarily maintained, in relative order of importance, by river discharge, the import of low salinity seawater through the Bering Strait, and net precipitation over the Ocean itself. This freshwater surface layer allows sea ice to form readily. In turn, the major exports of freshwater are the ice and water fluxes exiting Fram Strait and through the Canadian Arctic Archipelago. “Following the water” through the atmospheric, terrestrial and oceanic branches of the Arctic hydrologic cycle, and assessing links between these fluxes and the global climate system is a vibrant area of research. But the problem cannot be tackled all at once. Here, we focus on a large, yet digestible piece – precipitation, net precipitation and river discharge to the Arctic Ocean. Aspects of the ocean branch will be examined as part of Chapter 7. One of the important issues reviewed there is the link between the Fram Strait ice and water flux and deep water formation in the North Atlantic. As is evident in Figure 2.25, mean annual precipitation over the Arctic ranges widely. There is also strong seasonality. Precipitation over the Atlantic sector exhibits a general maximum during the winter half of the year while elsewhere a warm season maximum is the rule.
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 | 0.002 |
| 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) | 0.011 | 0.004 |
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".