Seasonally Dynamic Dissolved Carbon Cycling in a Large Hard Water Lake
Bibliographic record
Abstract
Abstract Inland waters play a crucial role in the global carbon cycle, with lakes integrating carbon from various sources within their catchment in addition to that fixed by local primary productivity. Isotopic measurements of carbon pools can differentiate contributions from these sources, with natural abundance radiocarbon (14C) a particularly powerful tool due to the large range in 14C characteristics among carbon sources. Here, we present 14C measurements of dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) from monthly water column samplings over the course of a year in Lake Geneva, a large oligotrophic hard water perialpine lake in Western Europe. We find that DIC in the lake is significantly 14C‐depleted relative to the atmosphere primarily due to the dissolution of carbonate rocks in the lake's catchment. Variability in DI14C is largely tied to the Rhône River inflow, where DI14C values were also found to vary seasonally. DOC has a 14C signature similar to that of DIC, reflecting the fact that much of the lake DOC pool is autochthonous. However, more 14C‐depleted DOC was observed in July and tied to increased river discharge from snow and glacier melt within the upper Rhône River basin. These observations shed light on carbon sources and dynamics within Lake Geneva and its alpine catchment and highlight the importance of preaged dissolved carbon inputs to the largest natural lake in Western Europe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".