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Record W3143753519 · doi:10.1029/2020wr027514

Fill‐and‐Spill: A Process Description of Runoff Generation at the Scale of the Beholder

2021· article· en· W3143753519 on OpenAlexaff
Jeffrey J. McDonnell, Christopher Spence, Daniel J. Karran, Ilja van Meerveld, C. J. Harman

Bibliographic record

VenueWater Resources Research · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsEnvironment and Climate Change CanadaGlobal Institute for Water SecurityUniversity of Saskatchewan
Fundersnot available
KeywordsSurface runoffScale (ratio)Process (computing)Environmental scienceEvent (particle physics)Range (aeronautics)OutflowComputer scienceUnit (ring theory)Hydrology (agriculture)Environmental resource managementGeographyGeologyEngineeringGeotechnical engineeringMeteorologyCartographyMathematicsEcology

Abstract

fetched live from OpenAlex

Abstract Descriptions of runoff generation processes continue to grow, helping to reveal complexities and hydrologic behavior across a wide range of environments and scales. But to date, there has been little grouping of these process facts. Here, we discuss how the “fill‐and‐spill” concept can provide a framework to group event‐based runoff generation processes. The fill‐and‐spill concept describes where vertical and lateral additions of water to a landscape unit are placed into storage (the fill)—and only when this storage reaches a critical level (the spill), and other storages are filled and become connected, does a previously infeasible (but subsequently important) outflow pathway become activated. We show that fill‐and‐spill can be observed at a range of scales and propose that future fieldwork should first define the scale of interest and then evaluate what is filling‐and‐spilling at that scale. Such an approach may be helpful for those instrumenting and modeling new hillslopes or catchments because it provides a structured way to develop perceptual models for runoff generation and to group behaviors at different sites and scales.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.289
Teacher spread0.233 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations163
Published2021
Admission routes1
Has abstractyes

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