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Record W2224022498

Developpement energetique par modelisation et intelligence territoriale: Un outil de prise de decision participative pour le developpement durable des projets eoliens

2013· article· fr· W2224022498 on OpenAlexaboutno aff
Vazquez Rascon, María de Lourdes

Bibliographic record

VenuePhDT · 2013
Typearticle
Languagefr
FieldEnvironmental Science
TopicScience and Climate Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

English later. La presente these porte sur le developpement et la mise a l’essai d’une approche participative et transparente d’aide a la decision permettant d’etablir des mesures d’insertion efficaces de projets de parcs eoliens. Cette approche se base a la fois sur un cadre argumentaire, et sur la prise en compte des systemes de valeurs et des preferences des acteurs impliques dans ces projets. Nous utilisons l’aide multicritere a la decision et les systemes d’information geographique, pour d’une part generer des scenarios correspondant a diverses possibilites d’implantation, y compris l’option de ne rien faire, et pour, d’autre part, evaluer ces scenarios en abordant les enjeux qu’ils soulevent, et en les modelisant sous forme de criteres et indicateurs. Les enjeux souleves par les acteurs permettent a la fois l’inclusion de differents aspects socioculturels, environnementaux et economiques des projets de parcs eoliens, et, leur priorisation selon les differentes visions de developpement durable qui leur correspondent, et conformement aux 16 principes de developpement durable inclus dans la Loi sur le developpement durable du Quebec This thesis focuses on the implementation of a participatory and transparent decision making tool about the wind farm projects. This tool is based on an (argumentative) framework that reflects the stakeholder’s values systems involved in these projects and it employs the multicriteria decision aide and the participatory geographical information systems, making it possible to represent this value systems by criteria and indicators to be evaluated. The stakeholder’s values systems will allow the inclusion of environmental, economic and social-cultural aspects of wind energy projects and, thus, a sustainable development wind projects vision. This vision will be analyzed using the 16 sustainable principles included in the Quebec’s Sustainable Development Act.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0070.006
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.053
GPT teacher head0.312
Teacher spread0.259 · 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 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

Citations1
Published2013
Admission routes1
Has abstractyes

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