Bibliographic record
Abstract
In a context of global changes, decline of biodiversity and increase of the urban population, the request of urban developers to integrate biodiversity into their practices is increasingly strong. My PhD thesis aimed to (1) make a review of the consideration of biodiversity in urban development, and (2) develop new tools to help developers to improve their practices. In the first part focused on biodiversity review, (1) we have expressed some doubts about the relevance of the use of current green roofs as possible integrated element of an ecological network; (2) The study of environmental measures implemented in 54 European eco-districts (mainly in France) showed that designers appeared to focus primarily on environmental benefits in terms of energy, transport, waste, water, and more rarely on biodiversity conservation; (3) LCA (life cycle analysis), a tool commonly used by developers to calculate the environmental impacts of a product (a green roof , a building or a district) integrates badly biodiversity in its calculations, and its use to compare different green elements could standardize practices which lead to an homogenization of biodiversity associated with the deterioration of ecosystem functioning. To help developers to better consider biodiversity in their practices, we have firstly contributed to the improvement of the tool «Profil-Biodiversité» created by Frank Derrien, and secondly, we have developed our own tool (BioDi(v)Strict) based on the diversity of habitats and the presence of four groups of bioindicator species to better reflect the ecological dynamic of a site. Both tools have been applied on a pilot site: the Cité Descartes (in Noisy-le- Grand and Champs-sur‐Marne). Finally, in order to let emerging a collective biodiversity awareness for the different local actors, we have developed a tool (NewDistrict) based on a multi‐agent system (MAS) model combined with a role-playing game constructed in a context of urban sprawl.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".