Developpement energetique par modelisation et intelligence territoriale: Un outil de prise de decision participative pour le developpement durable des projets eoliens
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".