Carsey Perspectives: Saving Salt, Protecting Watersheds, in Winter Road Maintenance. Highlights from a Social Venture Innovation Challege Winner
Bibliographic record
Abstract
In this Carsey Perspectives brief, author Andrew Jaccoma--who took first prize in the Community Track of the 2014 Social Venture Innovation Competition for his Sensible Spreader Technologies entry--explains the science behind the Coverage Indication Technology (CIT) created to increase road safety, increase efficiency, reduce wasteful dissemination of deicers, and lessen society’s impact on the environment. CIT uses cloud computing to share plowing, salt spreading, and location information throughout the entire fleet in real time. This empowers operators to make better decisions in the field and encourages the fleet to work together effectively. In addition, newer operators who may be less familiar with local routes can be aided by the mapping information that CIT provides. CIT has also proven useful in situations where one worker needs to cover another’s route. The Social Venture Innovation Challenge invites individuals and teams from across the state of New Hampshire to identify pressing social and/or environmental issues at the state, national, or global level, and then find an innovative business-oriented approach to solving them. The Challenge is organized by the Center for Social Innovation (CSIE) at the University of New Hampshire and is a joint venture between CSIE, the Paul College of Business & Economics, Carsey School of Public Policy, UNH Sustainability Institute, NH EPSCoR, UNH Innovation, and Net Impact UNH.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 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".