Travel time and source variation explain the molecular transformation of dissolved organic matter in an Alpine stream network
Bibliographic record
Abstract
Streams and rivers are important components of the carbon cycle as they simultaneously transport and transform terrigenous dissolved organic matter (DOM). The time DOM spends in a stream network is an important constraint on the biogeochemical processes that act upon DOM. We used high-resolution Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) to study the spatial distribution of DOM at the molecular level at more than 100 sites within an Alpine stream network during summer and winter baseflow. We developed a model approximating the time DOM spent in the fluvial network. Discharge-weighted travel time (DWTT) explained the compositional changes of DOM, which differed markedly in summer and winter. We attribute the seasonal differences to differences in source material. Hydrological mixing at confluences was an important driver of the spatial dynamics of DOM. From the spatial patterns of individual DOM compounds we inferred the distribution of sources within the catchment, which differed seasonally. Finally, we estimated the apparent mass transfer coefficients of individual DOM compounds at the network level and identified the oxidative state of DOM as an important factor explaining uptake efficiency. This work contributes to our understanding of the spatial processes, temporal constraints and chemical properties of DOM that regulate the transformation and diagenesis of DOM at the fluvial network scale.
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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) | 0.000 | 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".