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Record W4214863837 · doi:10.2172/1560613

R&D and Implementation Outcomes from the U.S.-India Bilateral Center for Building Energy Research and Development Program

2019· report· en· W4214863837 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLawrence Berkeley National Laboratory · 2019
Typereport
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversity of British Columbia
FundersDepartment of Science and Technology, Ministry of Science and Technology, IndiaU.S. Department of Energy
KeywordsBenchmarkingSoftware deploymentCapacity buildingJoint (building)Key (lock)Research centerEngineering managementEfficient energy useBusinessEngineeringArchitectural engineeringComputer sciencePolitical scienceEconomic growthMarketingEconomicsComputer securitySoftware engineering

Abstract

fetched live from OpenAlex

This paper explores the role of international partnerships to facilitate low-energy building design, construction, and operations. We present the strategic approach, joint research and development outcomes, and implementation activities of a unique U.S.-India program on buildings energy efficiency, the Center for Building Energy Research and Development. We discuss the collaboration successes in both countries despite their dissimilar building contexts, implementation challenges and opportunities. We highlight a range of R&D outcomes, such as novel tools and technologies developed and tested by the joint teams, with their technical energy savings potential, as well as results of capacity building and technology demonstrations. A deep-dive into key new scientific methods around building energy monitoring and benchmarking that could have a significant impact on high-performanceof buildings in both countries is also provided. Finally, in addition to joint R&D successes, pathways to deployment, and lessons learned are discussed as key takeaways.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.366
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it