Assessing the Possible Potential in Energy Consumption and Greenhouse Gas Emission: Application of a Proven Hybrid Method
Bibliographic record
Abstract
Energy evaluation together with greenhouse gas mitigation goes a long way in sustaining the growth and economy of a nation. Various evaluation methods have been adopted by researchers, academia and various country wise energy department ministries to achieve this aim. The most effective method is the hybrid evaluation method. This takes into consideration strength of a particular method to overcome the weakness of another method. This chapter focuses on a recently proven integrated method on energy and greenhouse gas studies-integrated IDA-ANN-DEA (index decomposition analysis-artificial neural network-data envelopment analysis). Case studies were exemplified using this approach in evaluating possible energy potential that could be saved in the manufacturing industries in Canada and South Africa as well as a particular food and beverage industry. Another case study focused on the amount of possible greenhouse gas that could be mitigated in the Canadian industry. The hybrid model proved very useful in its analysis.
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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 | 0.001 |
| 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 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".