Spatial variability of ocean fertilizing nutrients in the dust‐emitting ephemeral river catchments of Namibia
Bibliographic record
Abstract
Abstract Research into global hot spots of dust emission has focused on exposed fine‐grained sediments in palaeo‐ or ephemeral dryland lake basins including Etosha (Namibia) and Makgadikgadi (Botswana) in southern Africa. Namibia's western ephemeral river valleys are also known to produce dust but have remained largely overlooked as a regionally significant source. Nutrient enrichment of valley sediments and proximity to the South Atlantic suggests aeolian dust could play an important role in ocean fertilization. The fertility of valley dust is dependent on fluvial sediments originating in the upper catchments on the Southern African Central Plateau. In this study we investigate climate, geology, vegetation and land use variability and how these may influence the nitrogen, phosphorus and iron availability in the catchments. We intensely sampled the Huab, Kuiseb and Tsauchab river systems to map the spatial distribution of nutrients from upper catchments to river termini. Samples were analysed for the bioavailable fractions of iron, nitrogen and phosphorus as well as total nitrogen and phosphorus. Results show that the lower valley reaches are sources of aeolian dust enriched in nutrients. Nitrogen levels correlate with precipitation and vegetation levels and phosphorus levels with geology. However, differences in upper catchment sediment nutrient levels were not representative of downstream nutrient differences between valleys. Rather, it is the hydrological and geomorphological processes of the ephemeral river systems that are key for producing the enriched sediments in the lower reaches. We demonstrate that the ephemeral river valleys of western Namibia are an extensive and enriched source of mineral dust that could play a critical role in marine productivity of the southern Atlantic. © 2017 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".