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Record W7161762953 · doi:10.82308/11395

Hazen-Williams C-factor assessment in an operational irrigation pipeline

2001· dissertation· en· W7161762953 on OpenAlexaboutno aff
Connell, David, 1974-

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicIrrigation Practices and Water Management
Canadian institutionsnot available
Fundersnot available
KeywordsPipeline (software)Head (geology)DiagramPipeline transportRange (aeronautics)Flow (mathematics)Constant (computer programming)Line (geometry)Steady state (chemistry)

Abstract

fetched live from OpenAlex

In the spring of 1998, a closed end gravity flow pipeline was installed in the Lethbridge Northern Irrigation District. The pipes ranged from 900 mm to 300 mm in diameter. Manholes were placed at several locations along the pipeline and were used to install velocity meters and pressure transducers, which recorded data every second. Pressures and velocities during periods of steady state were used to calculate the head loss, Re and the Hazen-Williams friction factor, "C", along lengths of constant diameter. The results were compared to the industry design standard of C = 150 (used for the design of rigid PVC pipe installations) and to the theoretical hydraulically smooth line developed from the Moody diagram and the Darcy-Weisbach equation. Since the maximum and minimum calculated values of C came in the 750 mm and 900 mm diameter pipes, respectively, and all other C values, including those from the 300 mm and 650 mm diameters, fell between these, pipe diameter was assumed not to be a variable. Therefore all the field data was averaged. The average value of Re for the range studied was 9.73 x 105 . The corresponding best-fit C value is 147.7, which is 6.0% lower than the derived theoretical maximum. Since the derived theoretical maximum is the ideal condition and the recorded data was slightly lower and therefore determined be a good representation of what can be expected in true field conditions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.997

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.030
GPT teacher head0.304
Teacher spread0.274 · 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.

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

Citations0
Published2001
Admission routes1
Has abstractyes

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