Does urban land use decrease carbon sequestration?——A case study in Taizhou,China
Bibliographic record
Abstract
Aims Urban land use has dramatically changed ecosystem functions especially carbon sequestration and storage in the built-up area. Our objective was to assess carbon sequestration and storage by estimating the biomass and net primary productivity (NPP) of urban vegetation including trees,shrubs and lawns with consideration of the effects of garden management (pruning and mowing). Methods Biomass and NPP from all trees,shrubs and lawns were estimated from 346 random quadrats. Allometric equations were used to calculate tree biomass from diameter at breast height and total height. An increment borer was used to measure trunk increment. Biomass and NPP from shrubs and lawns were estimated by destructive sampling. Management activities were recorded through actual measurements and the records of gardeners. Important findings Trunk growth rate of individual urban trees is two times that in native forest. Pruning accounts for 30% of tree NPP. Carbon sequestration per unit of Taizhou built-up area is estimated to be 2.1 × 103 kg C.hm–2.a–1 (with trees,shrubs and lawns contributing 64%,9% and 27%,respectively),which is lower than native forest. We infer that the carbon sequestration ability of Taizhou built-up area could meet the level of native evergreen broad-leaved forest if vegetation coverage is increased from 23% to 46%.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".