MétaCan
Menu
Back to cohort
Record W2947674704 · doi:10.5539/eer.v9n1p71

Risk Evaluation of Bidding for Wind Farm Construction Project

2019· article· en· W2947674704 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnergy and Environment Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicValue Engineering and Management
Canadian institutionsnot available
Fundersnot available
KeywordsBiddingWind powerFuzzy logicComputer scienceEvaluation methodsRisk analysis (engineering)Empirical researchRisk managementBusinessEnvironmental resource managementEnvironmental economicsConstruction engineeringEnvironmental scienceReliability engineeringFinanceEngineeringMathematics

Abstract

fetched live from OpenAlex

With the rapid development and growth of the installed capacity of wind power generation in China, more and more attention has been paid to the risk of wind farm construction projects. Based on the extension element method and the multi-level fuzzy comprehensive evaluation method, this paper makes an empirical analysis on the bidding risk of the wind farm construction project of Xintiandi energy company in Hebei province. The research shows that the extensible matter element method can provide effective support for decision makers because there are no different evaluation principles and more accurate risk evaluation.

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.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.050
GPT teacher head0.288
Teacher spread0.237 · 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