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Record W1987100974 · doi:10.1002/sres.604

The Three Gorges Dam Project from a systems viewpoint

2004· article· en· W1987100974 on OpenAlexaff
Henry C. Alberts, Renée M. Alberts, Mitchel F. Bloom, A. Laflamme, Satu Teerikangas

Bibliographic record

VenueSystems Research and Behavioral Science · 2004
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsThree gorgesScheduleYangtze riverOperations researchProject teamCivil engineeringEngineering managementComputer scienceConstruction engineeringEngineeringChinaPolitical scienceKnowledge management

Abstract

fetched live from OpenAlex

Abstract The Three Gorges Dam on the Yangtze River is currently the largest construction project in the world. Because of its complexity, integration of social, environmental and technological systems, and tight coupling required by the schedule, the project should be managed using a systems approach. To find out if this was the case, the authors formed a research team, sponsored by the International Society of Systems Sciences, to tour the dam sight, interview managers and engineers, travel up the Yangtze to Chongqing, analyze problems posed by the critics of the dam and, finally, report back to the ISSS annual meeting in Shanghai following the trip. This paper reports on the team's observations, the results of the interviews and a systems model that analyzes the problems facing the dam builders. Finally, the paper concludes that the engineers and managers are aware of the interrelated problems and have planned for their solution. Copyright © 2004 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.554
GPT teacher head0.555
Teacher spread0.001 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations20
Published2004
Admission routes1
Has abstractyes

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