A meta-analysis of paleolimnological records reveals the sensitivity of lacustrine carbon burial rates to carbon sources and preservation conditions during the Anthropocene
Bibliographic record
Abstract
Inland waters are sites of active carbon (C) processing and transport along the land to ocean aquatic continuum (LOAC) that need to be accounted for closing the global C budget1,2. However, monitoring data are lacking and do not extend back as far as few decades, limiting our comprehension of the LOAC C cycle for the last centuries. Lake sediments provide a key archive for assessing C transport and transformation that occurs in lake catchments. Here, the analysis of large numbers of samples was performed on 420 lakes sediment records of the world to assess C burial rates and sources during the last 300 years. C and N (organic and mineral forms) on discrete samples were analyzed using a Variomax elemental analyzer to assess total C sequestration by lakes, C sources, and long-term changes in the contribution of allochtonous vs autochtonous sources to C transfers in lake-watersheds. Continuous sediment records were generated using core scanners (i.e. micro-XRF) and computed tomography to provide near-annual trends on terrigenous elements, here used as proxies of allochtonous sources (e.g. Al, Ti, K, Fe), and Mn:Fe ratio to infer past oxygen conditions3. Our results suggest that establishing a morphology-relevant lake typology that better characterises the types and distribution of oxygen conditions and terrigenous supplies across our sites is therefore the first step in providing a more robust evidence base for explaining the spatial-temporal variation in lake C burial rates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".