Arts, Humanities and Social Sciences Talking Points
Bibliographic record
Abstract
A playful way to achieve city-wide energy use reductions," at the 2016 Summer Study on Energy Efficiency in Buildings sponsored by the American Council for an Energy Efficient Economy (ACEEE) in Monterey, California.On behalf of the City of Fargo, and along with Dr. Huojun Yang (of NDSU's Department of Construction Management and Engineering) and Graduate Research Assistants Nick Braaksma and Hailong Zheng, Srivastava wrote a successful grant for $500,000 in funding from the ND Renewable Energy Council to be received and utilized by the City of Fargo for a fast-fill vehicle fueling station that will convert landfill gas to vehicle grade fuel for the City's solid waste vehicles.Srivastava also received a $46,800 grant from the ND Department of Commerce to continue efargo research and outreach.efargo is currently ranked third out of fifty US cities in the Georgetown University Energy Prize (GUEP).According to the GUEP dashboard (guep.iconics.com),since January 2015, Fargo residents and municipal buildings have lowered their electricity and gas use to save more than $4.8 million in utility bills and prevented 21,653,901 kg of carbon dioxide from entering the atmosphere.Srivastava serves as Project Lead for efargo.Key team members from NDSU include Peter Atwood (Technology Lead), Graduate Research Assistants
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.726 | 0.497 |
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".