Natural Variations in Dissolved Silicon Isotopes Across the Arctic Ocean From the Pacific to the Atlantic
Bibliographic record
Abstract
Abstract We present measurements of the silicon isotopic composition of silicic acid (δ30Si‐Si(OH)4) from seawater collected along a transect following one of the main flow paths of Pacific‐origin waters through the Arctic Ocean during the Canadian Arctic GEOTRACES and Distributed Biological Observatory expeditions in the summer of 2015. The δ30Si‐Si(OH)4signals track the modification of Pacific‐origin waters as they transit from west to east, and reflect the distribution of water masses, the dissolution of biogenic silica (bSiO2), and the biological utilization of Si in surface waters. Pacific‐origin waters have lower δ30Si‐Si(OH)4values than the surrounding water masses and are closely linked with a tongue of nutrient‐rich cold water that can be traced back to the Bering Strait. The δ30Si‐Si(OH)4measurements indicate that the high [Si(OH)4] in the deep waters of Baffin Bay are driven entirely by dissolution of exported bSiO2. Using an open‐system model, we estimate the biogenic Si isotope effect (30ε) for the Bering and Chukchi Seas, and the Canadian Arctic Archipelago to be30ε = −1.18 ± 0.02‰ (1SE), which agrees very well with laboratory and field estimates. In contrast, the isotope systematics in the Canada Basin and Baffin Bay are better represented by a closed‐system model. We also present an approach to estimate production by sea‐ice algae using δ30Si‐Si(OH)4signals, though a better understanding of the sea‐ice/water column Si systematics in the Arctic is needed to confirm the viability of this approach.
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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 | 0.001 |
| 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 | 0.001 | 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".