Recent developments integrating connected non-lotic and ephemeral water bodies into the Pulse-Shunt Concept
Bibliographic record
Abstract
River networks have been conceptualized as “leaky pipes” for carbon loss. However, there remains considerable uncertainty regarding where, when, and how carbon loss takes place along the aquatic continuum across hydroclimatic conditions. Recent modelling efforts have been developed to (1) connect river reaches with non- or semi-lotic systems including lakes, reservoirs, floodplains and wetlands, and (2) account for river network connectivity via quantification of ephemeral streamflow. These models, which use techniques such machine learning to scale from local measurements to high-resolution river network data products, enable the quantification of relative carbon loss fluxes in lentic vs. lotic systems across stream orders (“where”) within standardized hydroclimatic scenarios representing the full continua of flows and seasonal conditions possible within a watershed (“when”). These models further quantify carbon uptake via both biomineralization and photomineralization (“how”). We frame findings into an updated conceptual model of the Pulse-Shunt Concept, which builds on the representation of river networks as leaky pipes by correlating the “leakiness” with dependence on flow and stream order. We suggest that lakes and other lentic systems should be considered as reactivity “nodes” interspersed along mostly unreactive or passive river reaches. We additionally discuss how these river network modelling approaches can continue to be improved using sensor networks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".