Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Blockchain applications have continuously improved ever since its first debut on cryptocurrency. From then on, its uses have branched out from the financial realm, finding their way into numerous industries such as health, environmental, and governmental. Businesses are starting to take advantage of the intrinsic traits that made blockchain so notorious into their operations, such as security, integrity, and transparency. Blockchain’s versatility allows companies to cooperate on a shielded environment with business partners safely. This paper details how permissioned blockchain networks can accommodate collaborative business models securely to provide thriving business alliances. Examples of cooperative business models and business relationship orientation are described here, as well as how they generate value when paired with permissioned blockchain networks - a more business-oriented variety of blockchain. To support this study’s endeavors, business use cases are presented to highlight how simple it is to put in place a permissioned blockchain to businesses to achieve tighter bonds with business partners. The use cases contain particular goals that enterprises seek to accomplish by partnering up with other companies, as well as how, with the employment of blockchain, they can attain them.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it