Comparing the Early Stage Carbon Sequestration Rates and Effects on Soil Physico-Chemical Properties after Two Years of Planting Agroforestry Trees
Why this work is in the frame
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Bibliographic record
Abstract
Farm friendly and fast growing trees are the sustainable, cheaper and efficient source of carbon sequestration and carbon stock, however, their carbon sequestration potential vary among tree species depending upon several factors. This study was conducted to determine the carbon sequestration potential and carbon storage difference among different tree species at early stage. Second objective of this study was to observe the effects of trees on the physico-chemical properties of soils. Seedlings of fifteen widely planted farm trees species were planted under same set of climatic and soil conditions. Employing tree biomass after two year of planting (2014-2016), carbon stocks and carbon sequestration rates were calculated. Soil samples were collected under each tree species at two depths: 0-15cm and 16-30 cm, to determine the physico-chemical properties of soils such as pH, EC, N, P, K, C and organic matter (O.M.). It was found that Populus deltoides contained the highest carbon stocks (7.21 ± 1.31 kg C) and sequestered the CO2 at the highest rate of 13.21 ± 0.84 kg C/year as compared to all other fourteen tree species. O.M. (%) and Carbon (mg/kg) were also the highest in the soils under P. deltoides (2.29 ± 0.42 and 3.8 ± 0.2 respectively) as compared to and all other tree species. Nitrogen contents (%) were found the maximum in the soils under D. sissoo (0.063 ± 0.04) > Acacia nilotica (0.058 ± 0.008) and Albizia lebbeck (similar to Acacia nilotica). Such information enhances our capacity to better predict the carbon sequestration potential and carbon stock in different trees.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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