BIOMASS AND CARBON SEQUESTRATION ESTIMATION OF TREE SPECIES IN PUNIT VAN, GANDHINAGAR
Bibliographic record
Abstract
The most significant greenhouse gas on earth is carbon dioxide, which both absorbs and radiates heat. Earth’s temperature is increasing because of greenhouse gases and other human activities. The process of removing and storing carbon dioxide from the atmosphere is known as carbon sequestration. It is the only way to lessen atmospheric carbon dioxide in an effort to slow down the rate of climate change. The chosen study area for the research work is PUNIT VAN, located in Gandhinagar, Gujarat, India. Gandhinagar city is also known as Green city, because of the diversity in vegetation and rich biodiversity. The study was conducted with quadrate random sampling method. There were 20 quadrates taken of 10 m2. Total area of Punit van is 14.70 hectare. In the research, 27 species, including 317 individuals have been recorded in Punit Van. The field data of the trees analyzed using the random sampling of quadrate method, which shows that the dominant tree species in each quadrate is Azardirachta indica as total of 68 tree species in 13 quadrates. While the least dominant species found in only 1 quadrate were Ailanthus excelsa Roxb., Dalbergia latifolia, Ficus benghalensis, Holopteria integrifolia, Madhuca longifolia, Mangifera indica, Milletia peguensis, Peltophorum pterocarpum, Saraca asoca, Strychnos nux-vomica with the total number of species 1, 1, 4, 5, 7, 6, 3, 6, 13, 7 respectively. Total 41.5 tones carbon sequestered in a hectare.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".