Bibliographic record
Abstract
Involving landscape architecture inside the transport sector in France has been firmly established at the end of the XXth century. This was mainly supported by a current of thought being environmental friendly and aware of the importance of sustainable development. The place of a landscape architect has thus improved progressively. Nowadays the construction of an interurban road cannot go without the assessment of the environmental impacts. The assessment is directly implemented in the planning process, whithin the impact inspection, making it necessary to point out the environmental method used. Apart from that, there is no method suggested, neither in the jurisdiction nor in guides. You can observe a certain absence of rigor as well as superficiality in the methods precisions. The goal of this dissertation is to set the basics of a method improving the aspects of the landscape impacts. We will be inspired by Canadian ways of approach, more precisely from the work of Hydro-Quebec (1993) and by the theoretical work of Leduc & Raymond (2000) setting up a more rigorous method. The limits of this method as well as ways to be improved are presented in the end of this dissertation in order to open up a discussion and invite to continue the research on this subject.
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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 | 0.033 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".