Sustainability Assessment of Randullabad Watershed in Satara District of Maharashtra State, India
Bibliographic record
Abstract
Sustainability of watersheds being a major issue in India Kakade, 2017 proposed a new comprehensive framework and methodology for sustainability assessment of watersheds, which would also help design sustainable watershed projects. This new methodology was validated undertaking in-depth critical assessment of an integrated watershed development project implemented by Randullabad village Grampanchayat (Note 1) under the facilitation of BAIF (Note 2). Project of 836 ha area and 394 households was implemented during 2008 to 2013. The assessment was carried out to find out sustainability of social, economic and ecological domains at the baseline (2008), at project completion (2013) and five years after completion (2017-18). The indicators used in the framework and methodology by Kakade, 2017 was validated and the final framework emerged through the study has been presented in the paper. Rising trends of sustainability scores in all three domains were observed from inception to completion of project and also five years after completion. Key contributing factors for sustainability include the project design, community empowerment, post-project maintenance, governance and role of facilitating organizations.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".