Economically efficient and environment friendly energy management in rural area
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Energy management in rural area is a challenging issue for sustainable agriculture. A large number of livestock farms including dairy and poultry farms are the major cause of environmental pollution due to improper handling of animal and broiler wastes. The appropriate use of waste material not only reduces the environmental pollution but also serves as an additional source of electricity. In this article, efficient energy management model is developed to ensure uninterrupted supply of energy and also minimize the environmental impacts and cost of electricity to consumers by using multiobjective optimization. In the proposed scheme, electricity is supposed to be generated through biogas generation from waste materials of animals and broilers. In addition, electricity will also be reserved from solar, utility and diesel resources to ensure continuous supply of electricity to the consumers. Multiple sources of energy are being used to handle the intermittent nature of renewable resources and unpredicted electricity load shedding. The proposed multiobjective optimization is binary integer linear programming problem in which objective and constraints take binary and integer values. Branch and bound algorithm is incorporated to solve this multiobjective optimization problem.
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.
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 it