EVALUATION OF TREES AND THEIR CARBON SEQUESTRATION POTENTIAL USING NON-DESTRUCTIVE METHODS IN SURAT, GUJARAT
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
One important tactic for reducing climate change is carbon sequestration, which involves absorbing and storing carbon dioxide (CO₂) from the atmosphere. Abrama Road, Umbhel Garden, Sarthana Nature Park, Gorat, and Sneh Rashmi Botanical Garden are the five locations chosen for this study, which evaluates the carbon sequestration capability of urban trees in Surat city of Gujarat. A non-destructive technique based on girth at breast height (GBH), height, and biomass estimations was used to examine 73 different tree species. The study evaluates biomass buildup and carbon storage capability in several tree species across diverse urban environments using methods including remote sensing, allometric equations, and ground-based observations. The study contributes to climate resilience and sustainable urban planning by shedding light on the best tree species for sequestering carbon. These calculations emphasize how crucial afforestation is to improve carbon absorption. Policymakers may use the study's useful data to create sustainable urban forestry plans that will reduce CO2 emissions. Future studies should examine the effects of climate change and long-term sequestration trends on the growth of urban trees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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