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Record W2579457345 · doi:10.5539/emr.v6n1p39

An Evaluation of DEMATEL-EVM based Method for the Demonstration Projects of Water Environment Assessment

2017· article· en· W2579457345 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

VenueEngineering Management Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEvaluation Methods in Various Fields
Canadian institutionsnot available
Fundersnot available
KeywordsWeightingEnvironmental impact assessmentSet (abstract data type)Key (lock)Quality (philosophy)Computer scienceEnvironmental qualityIndex (typography)Engineering

Abstract

fetched live from OpenAlex

The existing methods of the water environmental quality assessment generally focus on the environmental characteristics, and fail to cover the core demands of ecological demonstration project. These methods lack a comprehensive analysis of sustainable development and maintenance capabilities. Also, the subjective analysis and computing of weight furthermore leads to unstable output. This paper is written in attempt to raise a set of assessment systems and methods to evaluate the demonstration projects of water environment. With the helping of establishing a complete quality evaluation index system of water environment demonstration project, the research approaches are based on the model of subjective and objective comprehensive weighting evaluation of DEMATEL-EVM. Through this method, it is able to analyze the key elements scientifically, which influence the water environmental project, finally makes the assessment more convincible.

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)0.026
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.582
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.186
GPT teacher head0.488
Teacher spread0.303 · 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