Baselining UBC Vancouver Campus Urban Forest and Land Use : Developing and Validating Up-to-date Tree Inventory and Land Classification Map of the UBC Vancouver Campus
Bibliographic record
Abstract
As urban forest provides ecological, social, and economic values to the residents, forest inventory can monitor forest health. Based on the land classification map of UBC Vancouver Campus, UBC Campus and Community Planning Team pays attention to tree health in the public green space. Working together, the forest inventory and land classification map are the priorities of urban planning and forest health in UBC. In order to solve the gap of no current inventory and land classification map on campus, this study aimed to update the UBC tree inventory and land classification map. R algorithms extracted individual trees’ parameters and LiDAR metrics using the latest UBC LiDAR data of 2018. Random forest classification was applied to determine the tree species (coniferous/deciduous) with the metrics. Four major land cover types were classified by the supervised classification scheme using the orthophoto of UBC in 2020. The major results showed that there are 14165 trees (crown diameter more than 4 m) on campus, and the height estimation by the LiDAR method had an overall accuracy of 80%. The campus’s total vegetation cover was 44%, which is higher than other towns in Vancouver. Considering the campus’s topography, coniferous trees on the southwest campus provided potential ecological roles of water retention. Due to the three major roads connected to campus, the northern campus undertook linking public transports. The study provided the basis for future studies of trees, vegetation, and UBC Vancouver Campus land planning. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".