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Design and analysis of inline pipe turbine

2020· article· es· W3023805074 on OpenAlexaff
Atif Saeed, Mustafa Jaffer, Hayyan Yousuf Khan, A. Haider, Wajahat Ali

Bibliographic record

Venue3C Tecnología_Glosas de innovación aplicadas a la pyme · 2020
Typearticle
Languagees
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsTurbineEngineeringMechanical engineeringNuclear engineeringForensic engineering

Abstract

fetched live from OpenAlex

In this current smart era, electricity is considered a necessity for development as almost all of machinery and circuitry runs on electrical power.Therefore, the production of electricity is a must for attaining progress.But nowadays, there is a constant struggle for access to large fossil reservoirs and the development for renewable resources is slow.There have been innovative inventions such as the wind turbines, water turbines, solar cells and many other renewable sources.These resources have slowed down the depletion of fossil fuels to a certain extent, but these inventions do have their shortcomings and most areas where energy harnessing is possible, are left unanswered.One such area where energy conversion is possible is in the water transportation system.To harness electrical energy from this system, a small turbine generator can be installed onto the pipelines to harness the kinetic energy of the flowing water in them.Hence forth by applying this research, another renewable energy resource is developed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.017
GPT teacher head0.250
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.

Study designSimulation or modeling
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
Published2020
Admission routes1
Has abstractyes

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