Bibliographic record
Abstract
This research analyzed the livelihood security of the rehabilitant farmers of Upper Krishna Project Area (UKP). The present investigation was undertaken in Upper Krishna Project (UKP) area of Bagalkot district, Karnataka state. Livelihood Security of the rehabilitant farmers was analyzed by considering five components viz., natural, physical, financial, human and social capital. All the rehabilitant farmers covering 176 villages and 136 rehabilitation centres spread over in Bagalkot, Bijapur, Belgaum, Gulbarga and Raichur districts under UKP form the population for the study. The present study depicted that Livelihood Security of the rehabilitant farmers was found to be 54.66 per cent. Natural capital of the rehabilitant farmers was found to be the lowest among all the capitals. Social capital performed moderately among the components of the livelihood security. Rehabilitant farmers residing closer, moderately and far away from the District Head Quarter (DHQ) also analyzed. Further study revealed that relatively higher Livelihood Security of 58.58 per cent was observed among the closely distanced rehabilitant farmers from the DHQ. Most of the rehabilitant farmers (35.00%) residing closer to DHQ belonged to high Livelihood Security category.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".