Urban Tree DBH Response to Fast Urbanization— A Case from Coastal City Zhanjiang, China
Bibliographic record
Abstract
Abstract Trees perform various ecosystem functions within urban green space, yet little is known about the magnitude of change in urban tree DBH, and its potential response to urbanization. Field investigation was used to determine current tree DBH within Urban Function Units (UFUs) in the coastal city Zhanjiang in China. The cover of each UFU was determined via visual interpretation of satellite images. We recorded 12,434 individuals within Zhanjiang green space belonging to 185 species, 137 genera, and 51 families. The dominant DBH range was 5-15 cm, which accounted for 43.72% of the total stems. The DBHs of 33 individuals were larger than 90 cm - 20 of these individuals were Ficus species. The average tree DBH within commercial areas was (32.29 cm ±1.74 cm), which was the highest among all UFU types, and lowest within woodland areas (7.11 cm ± 0.56 cm). Tree DBH was significantly positively correlated with imperious surface rate, and significantly negatively correlated with green space surface rate. Variation partitioning analysis showed that impervious surface rate had the highest explanatory power, followed by construction age, then patch density. These three prediction variables, however, only explained 20% of the total observed variation - this suggests that DBH was strongly influenced by several additional factors. Understanding urban tree DBH structure and its influencing factors can promote the stable development of the urban forest.
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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 | 0.001 |
| Scholarly communication | 0.000 | 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".