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Record W2049002218 · doi:10.1088/1755-1315/22/0/001001

The 27th IAHR Symposium on Hydraulic Machinery and Systems (IAHR 2014)

2014· article· en· W2049002218 on OpenAlexaboutno aff
N Désy

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2014
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsnot available
Fundersnot available
KeywordsHydroelectricityHydropowerPopulationEvent (particle physics)ElectricityGeographyBusinessEngineeringSociology

Abstract

fetched live from OpenAlex

On behalf of the Organizing Committee and myself, it is my pleasure to welcome you to the 27th Symposium on Hydraulic Machinery and Systems. We are glad to welcome you in Montreal, Canada, in late September, just as the city welcomes the fall season and the trees start to show their nice colors. The Symposium will take place at the Hotel Omni Mont-Royal, located in the heart of the city. Participants will then have the opportunity to discover the many charms of Montreal, a rich blend between European and American cultures with diverse culinary offers and its legendary hospitality, nightlife and unique attractions. But other than the charm of Montreal, why are we holding it in Canada? · Canada is a world leader in hydropower production, with an installed capacity of over 70,000 megawatts (MW) and an annual average production of 350 terawatt-hours (TWh). · Thus, Canada is one of the world's largest producers of clean, renewable hydroelectric power. · Hydropower accounts for 97% of Canada's renewable electricity generation and nearly 13% of the world-wide production of hydropower, but with 0.5% of the world population. · Approximately 60% of the electricity generated in Canada in 2008 came from hydroelectric power plants. And there is the potential to more than double the hydroelectric capacity in Canada. Our Organizing Committee was formed in November 2011 and has undertaken a number of important steps to ensure that this event will be a success. We see this event as a very important one to help create personal networks and transfer knowledge to the younger generation of scientists. Bienvenue at our 2014 ‘‘rendez-vous’’ . Normand Désy Canadian Representative IAHR Executive Committee

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.541

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 studies0.0010.001
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 head0.007
GPT teacher head0.174
Teacher spread0.167 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

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