Biomass-Derived Provenance Dominates Glacial Surface Organic Carbon in the Western Himalaya
Bibliographic record
Abstract
The origin, transport pathway, and spatial variability of total organic carbon (OC) in the western Himalayan glaciers are poorly understood compared to those of black carbon (BC) and dust, but it is critically important to evaluate the climatic role of OC in the region. By applying the distribution of OC activation energy; 14 C activity; and radiogenic isotopes of 208 Pb/ 204 Pb, 207 Pb/ 204 Pb, and 206 Pb/ 204 Pb in glacial debris and atmospheric particulate matter (PM 10 size fraction), we demonstrate that 98.3 ± 1.6 and 1.7 ± 1.6% of OC in western Himalayan glaciers are derived from biomass and petrogenic sources, respectively. The δ 13 C and N/C composition indicates that the biomass is a complex mixture of C3 vegetation and autochthonous photoautotrophic input modified by heterotrophic microbial activity. The data set reveals that the studied western Himalayan glacier has negligible contributions from fossil-fuel-derived particles, which contrasts to the central and eastern Himalayan glaciers that have significant contributions from fossil fuel sources. We show that this spatial variability of OC sources relates to regional differences in air mass transport pathways and precipitation regimes over the Himalaya. Moreover, our observation suggests that biomass-derived carbon could be the only primary driver of carbon-induced glacier melting in the western Himalaya.
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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 | 0.001 | 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".