Aquatic biomass is a major source to particulate organic matter export in large Arctic rivers
Bibliographic record
Abstract
Arctic rivers provide an integrated signature of the changing landscape and transmit signals of change to the ocean. Here, we use a decade of particulate organic matter (POM) compositional data to deconvolute multiple allochthonous and autochthonous pan-Arctic and watershed-specific sources. Constraints from carbon-to-nitrogen ratios (C:N), δ 13 C, and Δ 14 C signatures reveal a large, hitherto overlooked contribution from aquatic biomass. Separation in Δ 14 C age is enhanced by splitting soil sources into shallow and deep pools (mean ± SD: −228 ± 211 vs. −492 ± 173‰) rather than traditional active layer and permafrost pools (−300 ± 236 vs. −441 ± 215‰) that do not represent permafrost-free Arctic regions. We estimate that 39 to 60% (5 to 95% credible interval) of the annual pan-Arctic POM flux (averaging 4,391 Gg/y particulate organic carbon from 2012 to 2019) comes from aquatic biomass. The remainder is sourced from yedoma, deep soils, shallow soils, petrogenic inputs, and fresh terrestrial production. Climate change-induced warming and increasing CO 2 concentrations may enhance both soil destabilization and Arctic river aquatic biomass production, increasing fluxes of POM to the ocean. Younger, autochthonous, and older soil-derived POM likely have different destinies (preferential microbial uptake and processing vs. significant sediment burial, respectively). A small (~7%) increase in aquatic biomass POM flux with warming would be equivalent to a ~30% increase in deep soil POM flux. There is a clear need to better quantify how the balance of endmember fluxes may shift with different ramifications for different endmembers and how this will impact the Arctic system.
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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 | 0.000 |
| 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".