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Record W4414344877 · doi:10.1002/wat2.70036

Knots in the Strings: Do Small‐Scale River Features Shape Catchment‐Scale Fluxes?

2025· article· en· W4414344877 on OpenAlexaff
Ellen Wohl, Martyn Clark, Li Li, Chris Soulsby, Doerthe Tetzlaff

Bibliographic record

VenueWiley Interdisciplinary Reviews Water · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBiogeochemical cycleHomogeneousHydrology (agriculture)Natural (archaeology)River managementPatch dynamicsSpatial ecologySpatial variability

Abstract

fetched live from OpenAlex

ABSTRACT Field evidence suggests that reach‐scale heterogeneities in river corridors can strongly influence catchment‐scale dynamics including material fluxes and biogeochemical transformations. However, spatial effects and the emergence of processes are not commonly incorporated into catchment‐scale hydrological and biogeochemical models. We differentiate river reaches as strings—relatively simple, homogeneous reaches with limited lateral and vertical connectivity—or knots associated with bifurcations, confluences, and obstructions, which are spatially and temporally heterogeneous reaches in a river network. We explore how knots affect reach‐scale processes including flow attenuation, enhanced vertical and lateral connectivity, and augmented solute retention and uptake. We discuss how the simplifications associated with common models might affect both understanding river corridors and river networks, and management designed to increase resilience to natural hazards. We emphasize the need to better understand how small‐scale heterogeneities cumulatively influence catchment‐scale dynamics. Case studies from Scotland and Germany illustrate the effects of knots and the need to capture knot‐related nonlinearities in hydrological and biogeochemical modeling. We highlight data challenges in related modeling, including: the availability, quality, and resolution of the source data that map knots and strings; the dependence of processes on the physical structure of the river network and how river corridor reaches are connected in multiple dimensions that may be impossible to measure directly; and the need for interdisciplinary efforts to develop integrated, high‐resolution spatial datasets and co‐located, high‐frequency functional data across both time and space. We end by suggesting how to incorporate knots in large‐domain models. This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water and Environmental Change Water and Life > Conservation, Management, and Awareness

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.012
GPT teacher head0.269
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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