Modelling the Potential Impacts of Climate Change on Arboreal Diversity of the University of British Columbia Vancouver Campus from 2050 to 2080
Bibliographic record
Abstract
Climate change poses a considerable risk to forest diversity in urban communities. The University of British Columbia (UBC) seeks to identify vulnerable tree species on campus and apply strategies for sustaining arboreal diversity in future decades. In contribution to these efforts, this study investigated the potential shift in tree composition on the UBC Vancouver Campus over the next century by (1) predicting suitable climatic habitat for 128 campus species in 2050 and 2080, and (2) mapping the expected change in species richness across North America. Species observation data from the Global Biodiversity Information Facility (GBIF) and climate predictors from ClimateNA were used to perform MaxEnt species distribution modelling. The climatic baseline (1961-1990) range for each tree species was defined using average annual temperature and precipitation, and projected using RCP 8.5 climate scenarios for 2050 (2041-2070) and 2080 (2071-2100). This dataset contains all geoprocessing scripts, species range and diversity maps for the baseline year, 2050 and 2080, as well as model outputs for each tree species.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".