Blockchain’s potential to address issues in genomics research and how it is being used today
Bibliographic record
Abstract
Advancements in technology have greatly decreased the costs of genome sequencing and expedited the entire sequencing process. As a result, there has been a significant increase in the volume of genomic data. Although this is useful for genomics research, there are two major concerns with this increase in data. First, the greater volume of genomic data requires a substantial amount of computational resources to process and store this data. While cloud services can seem like an effective solution to process and store this data, cloud services aggregate their information in one data center which results in the risk of a single point of failure. With the increase in genomic data, there is also an increase in privacy concerns because genomic data contains personal and sensitive information. People are not comfortable with large companies that store genomic data and people do not want this data shared with the public. Blockchain is a network that can utilize numerous computers to process data and store multiple copies of the database to eliminate the risk of a single point of failure. The blockchain is also a decentralized network which means that it is not regulated by a third party. This allows the data contributors to have full ownership of their genomic data and can decide who can access it. Today, there are several companies that have realized the advantages of blockchain and adopted this technology to store genomic data and give data contributors full control over this data.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".