Roadmap for harmonization of energy efficiency standards in south asia \n- report on the second sari/energy harmonization meeting
Bibliographic record
Abstract
Nexant SARI/Energy would like to thank several people for the valuable insights and sources of information, which led to the development of the Second Meeting on Harmonization and the production of this report.Some of the members of the community of international efficiency experts in the region who helped provide the basis for the analysis were Peter du Pont, Michael Philips, and Tanmay Tathagat.Particular insights were gained from reports by, and conversations with, Lloyd Harrington and Paul Waide.Finally, conversations with Peter Biermayer were indispensable.Most importantly, we appreciate the input regarding the status and goals of regional efficiency programs provided by regional stakeholders.In particular, the attendees of the meeting provided the effort and insight needed to move forward with efficiency programs in the region, in both the preparation of presentations made during the proceedings and in the stimulating and productive discussions that followed.Participants to the meeting and other contributors to the report include members of the Indian Bureau of Energy Efficiency, Bureau of Indian Standards, Bangladesh Standards and Testing Institution, Bangladesh Power Cell, Ceylon Electricity Board, Sri Lanka Standards Institution, and Nepal Bureau of Standards and Metrology.Particular thanks go to the Engineering staff of the Demand Side Management branch of the Ceylon Electricity Board.Nexant SARI/Energy would also like to acknowledge the contribution of CLASP to the report and the harmonization activities undertaken under SARI/Energy technical assistance program.
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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 | 0.024 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".