A mechanistic‐based framework to understand how dissolved organic carbon is processed in a large fluvial lake
Bibliographic record
Abstract
Lay Abstract Dissolved organic carbon (DOC) is a fundamental component of the biogeochemical cycling of nutrients in aquatic ecosystems and is the main carbon source supporting bacterial production. The efficiency at which heterotrophic (nonphotosynthetic) bacteria convert this substrate into biomass depends mainly on the quality of DOC in the water column. DOC is constantly processed through various physical, chemical, and biological mechanisms that operate simultaneously and alter its quality. It is paramount to understand how these different processes interact to drive the fate of DOC in aquatic ecosystems. Based on field data collected in a large fluvial lake, we developed and validated a mechanistic model that provides a framework to understand the relative contribution of the main processes involved in both labile (DOCL) and semilabile (DOCSL) DOC pool kinetics. The model revealed that during the downstream flow, each category of DOC pool was processed differently by bacteria: DOCL was preferentially used for biomass production, whereas DOCSL completed bacterial carbon demand. Our results also suggest that a decrease in DOCL abundance will further determine the intake of DOCSL.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".