Comparing the Level of Above-ground Carbon Sequestration and Respective Ecosystem Services between Native and Cultivated Trees in the University of British Columbia Botanical Garden
Bibliographic record
Abstract
Urban trees play an essential role in alleviating the local effects of global warming by sequestering atmospheric carbon dioxide, regulating temperature and reducing air pollution in urban areas. There have been studies estimating carbon sequestration of urban trees using Light Detection and Ranging (LiDAR). Yet, neither of the studies made comparison between species nor extended the discussion towards ecosystem services. Therefore, this study compared the attributes between native and cultivated trees in the Asian Garden of the University of British Columbia (UBC) Botanical Garden, estimated their level of above-ground carbon sequestration, and thus examined respective implications in terms of ecosystem services. A canopy height model was first generated from LiDAR point cloud data for individual tree segmentation based on Dalponte algorithm. There were 803 derived trees matched successfully with the field-measured trees in ArcGIS with an accuracy of 69.5%. The level of above-ground carbon sequestration of each tree was estimated from tree height and canopy area using a non-linear regression model. Results showed that the average carbon sequestration level of native trees was higher than cultivated trees by 1102 kg (p = 3.045 x 10-7), which facilitated more effective soil formation. The average canopy areas of both trees were approximately 63 m2 (p = 0.696), which gives rise to their similar contribution in temperature cooling and air purification. In general, the garden provides a high level of biodiversity and education value thanks to its high species diversity. Regarding future directions in garden management, the removal of native trees and the planting location of cultivated trees in the garden should be carefully evaluated since they have higher level of ecosystem services and higher crown area to height ratio respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".