Controls of longitudinal variation in δ<sup>13</sup>C-DIC in rivers: A global meta-analysis
Bibliographic record
Abstract
Abstract. We conducted a literature survey to investigate controls and spatial and temporal patterns of δ13C-DIC and deviations between δ13C-DIC and the δ13C signature of DIC at isotopic equilibrium with the atmosphere (Δδ13C-DIC) in streams and rivers throughout the world. We used generalized additive mixed models to relate Δδ13C-DIC and δ13C-DIC in lotic ecosystems to ecological variables including elevation, Strahler order, and partial pressure of dissolved CO2 (pCO2), and to examin e seasonal shifts in Δδ13C-DIC and δ13C-DIC over a range in latitude. Elevation, Strahler order, and DIC concentrations explained a large fraction of the variation in Δδ13C-DIC, and these variables plus pH and pCO2 explained much of the variation in δ13C-DIC. Seasonal fluctuations in δ13C-DIC were most apparent in rivers located in temperate regions with seasonal snow cover. Small streams tended to have lower δ13C-DIC values than large rivers. Overall, our analysis indicates that processes that add CO2 to the water column, including groundwater inputs, decomposition, and respiration, should have a greater influence on δ13C-DIC than processes that remove CO2. Both physical (gas exchange with the atmosphere, weathering, ice cover) and biolog ical (respiration in regions with high C4 grass abundance, photosynthesis by cyanobacteria) processes appear to control δ13C-DIC in streams and rivers, but the relative importance of these processes shifts from upstream to downstream.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".