Dissolved aluminum in the northeast Pacific and the western Arctic Ocean
Bibliographic record
Abstract
Aluminum dynamics in the ocean are unique given its rapid removal from the surface through adsorption onto sinking particles and its high abundance in the earth's crust. We optimized a flow injection analysis method to allow detection of the extremely low concentrations of dissolved aluminum found in the study region. Samples from the northeast Pacific have the lowest concentrations of dissolved Al found anywhere in the world’s ocean. We present dissolved aluminum data from a transect along Line P in the northeast Pacific in 2010 and 2011. Stations extend from near coastal waters, going through areas of potential eddy influence, and terminate at the historic Ocean Station Papa. Open ocean data shows concentrations an order of magnitude lower than extensive work performed in the Atlantic but overall similar trends with higher aluminum in the surface layer. The concentrations can go as low as 0.06 nmol/kg at depth and 0.36 nmol/kg in the surface. Coastal stations show elevated aluminum levels relative to the open ocean, and eddy influence inputs high aluminum concentrations from coastal shelf water. In the western Arctic, we present a transect from the 2009 Canadian GEOTRACES program. Here we see concentrations that increase with depth, from below 1.0 nmol/kg at the surface up to 11.6 nmol/kg at depth. This is inconsistent with previous measurements in the region, which showed an increase in the surface, proposed to be due to sea ice melt supplying dissolved aluminum to the surface. The high concentration of dissolved aluminum at depth indicate a bottom source of dissolved aluminum in this region. This is also observed in the north Atlantic near Labrador. Lastly, we present dust input data from both of these regions and compare them to previous modelled data, showing that models in the Pacific over-represent the amount of dust input. Arctic data is relatively less well known for dust.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 teacher head, 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".