Biodiversité urbaine : portraits de Montréal, Bordeaux et Barcelone
Bibliographic record
Abstract
« À l’heure où l’on s’inquiète de l’effet des changements climatiques et de l’urbanisation croissante, une multitude d’initiatives audacieuses émergent dans plusieurs villes de la planète. Les espaces verts urbains apportent un bien-être psychologique, favorisent les interactions sociales et promeuvent des modes de vie sains. Pourtant, la fragmentation conséquente du paysage urbain implique généralement une taille restreinte pour ces espaces verts. Ces derniers n’en restent pas moins importants, notamment pour les petits organismes (petits animaux, insectes, microorganismes). Un même fragment de végétation (ex. : haie) peut être à la fois un habitat (pour un oiseau), un corridor (pour un petit mammifère), un mur (pour un insecte) ou tout un écosystème (pour un microbe). Il est donc primordial d’étudier l’écosystème urbain, sa biodiversité et ses fonctions. Afin d’inspirer des projets d’adaptation urbaine, durables et favorisant la biodiversité, cet article propose une vitrine de projets issus de trois métropoles : Montréal, Bordeaux et Barcelone. [...] »
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: yes | Other design | low |
| opus | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: yes | Other design | low |
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".