A blockchain-based user-centric emission monitoring and trading system for multi-modal mobility
Bibliographic record
Abstract
A new design of a user-centric Emission Trading Systems (ETS) and its implementation as a carbon Blockchain framework for Smart Mobility Data-market (cBSMD) is pre-sented. The cBSMD allows for individual transactions of token-based GHG emission quantities when realizing a trip in a multimodal setting as well as the management of system-wide emission performance data. The cBSMD design is here applied to an ETS framework where individual travellers receive a certain amount of emission credits in the form of tokens. Travellers spend tokens every time they emit GHG when travelling in a multi-modal network through cBSMD transactions. This design instance of cBSMD is then applied to a case-study of 24hours of mobility for 3,187 travelers. The cBSMD performs with a very low latency and high throughput for this number of travelers. To showcase cBSMD data management features, socio-demographic and trip features regarding token usage and emission performance are also analyzed. Our proposed system sets the first implementation step towards the design of future user-centric and practice-ready ETS frameworks.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".