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Record W2066267332 · doi:10.1002/hyp.95

Portable irrigation system for studying hillslope and wetland runoff generation processes

2001· article· en· W2066267332 on OpenAlexaff
J. M. Waddington, K. J. Devito

Bibliographic record

VenueHydrological Processes · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversity of AlbertaMcMaster University
Fundersnot available
KeywordsEnvironmental scienceHydrology (agriculture)HydrographIrrigationSurface runoffPrecipitationTRACERMeteorologyGeology

Abstract

fetched live from OpenAlex

Abstract The advantage of an irrigation system is that experiments of varying precipitation duration and intensity can be performed in a controlled situation. An effective portable irrigation system is described for use in experimental plot hillslope and wetland runoff studies. The system consists of four parts:(1) a water pump; (2) a tracer reservoir; (3) a chemical feed pump; and (4) distribution hoses. Relatively uniform water application is achieved by a series of manual valves controlling water flow from the main carrier hose to the distribution hoses. The irrigation system materials are inexpensive and installation and operation costs are minor. The system was used to study runoff generation from a small‐saturated area in a spring‐fed swamp for a range of precipitation intensities and durations. The irrigation system applied a maximum intensity of 14·0mm h −1 , for a maximum duration of 180 min, to a 190m 2 area. This range of application incorporated all storms up to a one in three year event. The variance of the tracer load was almost three times greater for natural (60%) than with the irrigation system (22%). The irrigation system reduced the uncertainty in the pre‐event water fraction (using a two‐component hydrograph separation) from 3·4% in natural events to only 0·7%. The irrigation system design, operation, calibration and cost are presented. Copyright © 2001 John Wiley & Sons, Ltd.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

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

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 head0.033
GPT teacher head0.237
Teacher spread0.204 · 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 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

Citations6
Published2001
Admission routes1
Has abstractyes

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