Significance of Reducing Emissions from Degradation and Deforestation (Redd): Evidence from Study in Terai Region of India
Bibliographic record
Abstract
Reduced Emissions from Deforestation and Degradation (REDD) has became a major issue in international climate change negotiations. REDD was first introduced by United Nations Framework Convention on Climate Change (UNFCCC) at its 11th session of Conference of Parties (COP) held in Montreal, Canada in December 2005. However, till date a consensus on making REDD practicable and marketable mechanism has not been reached. There are differences between developing countries having rich tropical forest cover. There are issues associated with methodologies, monitoring, internal forest policy, indigenous rights etc. In the present paper efforts were made to demonstrate the significance of natural and existing forests in sequestering and storing carbon. The carbon sequestration and storage potential is much higher in natural forests compared with two plantations viz of Dalbergia sissoo and Terminalia arjuna. But enhancing land area under new forest cover can also be not refuted. Thus conserving natural forest coupled with adding new areas under forest through plantation should be the strategy for reversing and/or reducing global warming and climate change.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".