Carbon Isotope Composition and Geochemical Features of Sediments From Gongga Mountain, China, and Potential Environmental Implications
Bibliographic record
Abstract
Using gas chromatography-triple quadrupole tandem mass spectrometry (GC-MS/MS), the soluble organic matter was analyzed for the first time in twenty-two sediment samples from the eastern slopes of the Gongga Mountain, China, at high altitudes between 4,600 and 6,700 m. The C11-C33n-alkanes and C9-C33n-alkan-2-ones were identified in these samples. Both compounds were dominated by odd carbon numbers in the long-chain molecules and contained a maximum of n-C27 or n-C29, indicating that the sediments were predominantly of higher plant origin. However, the short-chain n-alkan-2-ones, with a maximum content of n-C17 or i-C18 (phytone, 6, 10, 14-trimethylpentadecan-2-one), did not show a predominance of odd and even numbers, suggesting that they were predominantly derived from bacteria and algae. Therefore, we suggest that the organic matter in Gongga Mountain comes from three sources, i.e. bacteria, algae, and higher plants. Stable carbon isotope (δ13C) values ranged from −24.6‰ to −27.3‰, indicating that C3 plants were the dominant organic input to the sediments and suggesting a relatively colder and drier depositional environment. However, C4 plants increase sharply at high altitudes of 6,300–6,600 m, suggesting that the paleoclimate of Gongga Mountain became drier and wetter with the increase of altitude.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".