Pan‐Arctic patterns in black carbon sources and fluvial discharges deduced from radiocarbon and PAH source apportionment markers in estuarine surface sediments
Bibliographic record
Abstract
A pan‐arctic geospatial picture of black carbon (BC) characteristics was obtained from the seven largest arctic rivers by combining with molecular combustion markers (polycyclic aromatic hydrocarbons) and radiocarbon (14C) analysis. The results suggested that the contribution from modern biomass burning to BC ranged from low in the Yukon (8%) and Lena (5%) Rivers to high in the Yenisey River (88%). The Mackenzie River contributed almost half of the total arctic fluvial BC export of 202 kton a−1 (kton = 109 g), with the five Russian‐Arctic rivers contributing 10–36 kton a−1 each. The 14C‐based source estimate of fluvially exported BC to the Arctic Ocean, weighted by the riverine BC fluxes, amount to about 20% from vegetation/biofuel burning and 80% from 14C‐extinct sources such as fossil fuel combustion and relict BC in uplifted source rocks. Combining these pan‐arctic data with available estimates of BC export from other rivers gave a revised estimate of global riverine BC export flux of 26 × 103 kton a−1. This is twice higher than a single previous estimate and confirms that river export of BC is a more important pathway of BC to the oceans than direct atmospheric deposition.
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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 | 0.000 | 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 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".