Arbres privés urbains de la ville de Québec (Urban private trees of Quebec City)
Bibliographic record
Abstract
Trees in urban areas contribute significantly to air quality, help moderate extreme heat, and support the physical and mental well‑being of residents, making cities more liveable. In Québec City, however, our understanding of the urban forest remains incomplete. While the City maintains a detailed inventory of trees on municipal land, trees located on private properties and in residential yards are largely absent from existing records. Gaining a clearer picture of species composition and tree health across the entire urban landscape is important for informed forest management, particularly in light of potential pressures such as pests or diseases that could affect canopy cover. This project focuses on documenting urban trees located on private property through a collaborative initiative involving the City of Québec, the Association forestière des deux rives, the Collectif Canopée, Cégep de Sainte‑Foy, the Port of Québec, and CERFO. The approach combines citizen/participatory science, with residents contributing information about trees on their properties, and targeted data collection carried out with participating businesses and institutions. The dataset was collected and is curated by the Chaire de recherche sur l’arbre urbain et son milieu (CRAUM). It is an evolving dataset that continues to grow as new observations are added and reviewed. The data are shared under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Any reuse of the dataset must acknowledge CRAUM and the listed authors, include a citation to the dataset, and clearly indicate whether modifications have been made. Citation to be used: Lescoulie, A., Bissegger O’Connor, S., Bernard, A., & Sivarajah, S. (2026). Arbres urbains privés de la ville de Québec (Urban Private Trees of Québec City). Chaire de recherche sur l’arbre urbain et son milieu (CRAUM). Open Science Framework. https://doi.org/10.17605/OSF.IO/57BCJ
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.009 |
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".