The distribution of neodymium isotopes in Arctic Ocean basins
Bibliographic record
Abstract
Nd concentration and isotope data have been obtained for the Canada, Amundsen, and Makarov Basins of the Arctic Ocean. A pattern of high Nd concentrations (up to 58 pM) at shallow depths is seen throughout the Arctic, and is distinct from that generally seen in other oceans where surface waters are relatively depleted. A range of isotopic variations across the Arctic and within individual depth profiles reflects the different sources of waters. The dominant source of water, and so Nd, is the Atlantic Ocean, with lesser contributions from the Pacific and Arctic Rivers. Radiogenic isotope Nd signatures (up to eNd =!6.5) can be traced in Pacific water flowing into the Canada Basin. Waters from rivers draining older terrains provide very unradiogenic Nd (down to eNd =!14.2) that can be traced in surface waters across much of the Eurasian Basin. A distinct feature of the Arctic is the general influence of the shelves on the Nd concentrations of waters flowing into the basins, either from the Pacific across the Chukchi Sea, or from across the extensive Siberian shelves. Water–shelf interaction results in an increase in Nd concentration without significant changes in salinity in essentially all waters in the Arctic, through processes that are not yet well understood. In estuarine regions other processes modify the Nd signal of freshwater compo-nents supplied into the Arctic Basin, and possibly also contribute to sedimentary Nd that may be subsequently involved in sediment–water interactions. Mixing relationships indicate that in estuaries, Nd is removed from major river waters to differ-
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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 | 0.001 | 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) | 0.000 | 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".