Paysages Humanisés : Quels enjeux de biodiversité au sein de ces territoires ?
Bibliographic record
Abstract
Au Quebec, le Ministere du Developpement Durable, de l'Environnement et des Parcs, a cree et inscrit en 2002 dans la loi sur la conservation du patrimoine naturel, un nouveau type d'aire protegee. Nomme paysage humanise, ce statut a pour but la protection de la biodiversite sur les territoires habites pour lesquels les ressources sont exploitees durablement. Une nouvelle approche de conservation voit le jour conciliant maintien des activites humaines, mise en valeur du territoire, engagement des communautes et protection de la biodiversite. Le paysage humanise est donc un outil innovateur qui est en contraste avec la vision conversationniste qui a longtemps etait le fer de lance dans l'etablissement d'aires protegees strictes au Quebec. Cette presente etude vise a determiner les enjeux au sein des territoires ruraux et a mener une reflexion sur la mise en application et la gestion future de l'agriculture dans ce type d'aires. Ces recherches ont permis, au final, d'etablir un certain nombre de recommandations a Nature Quebec pour son implication future dans la mise en oeuvre du paysage humanise, comme la participation a la creation d'une collaboration interministerielle, d'un guide d'aide a la realisation d'un paysage humanise disponible pour les communautes etc...
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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, 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".