Environmental Contributions of BTCEN Project: Sustainability with Blockchain
Bibliographic record
Abstract
The BTCEN project has begun to leverage blockchain technology, an innovation to increase efficiency and transparency in supply chain activities, promote recycling, and improve environmental sustainability. BTCEN has integrated Blockchain technology with platforms of e-commerce, tokenization, CRM, and ERP modules into its own systems. In this way, it increased data security while creating an effective tracking system. As a main activity, BTCEN recycles the beverage bottles of product users, increases the participation rate and conversion amount through gamification and some tangible rewards, and uses "Bring Back (BB) Coin" and NFTs as tools. Recycling vending machines strategically placed in different local centers make the process convenient and interesting, while additional incentives such as discounts encourage sustainable behavior. Awareness campaigns in various forms and partnerships with some environmental organizations, educational institutions, and local governments will support BTCEN's successes. BTCEN aims to combine ecological balance and sustainability methods while using technological innovations in its activities. By setting a standard that combines all these, it also encourages the social responsibility culture necessary for a clean environment to be left to future generations
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".