Does urban land use decrease carbon sequestration?——A case study in Taizhou,China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it