Author Correction: The role of blockchain to secure internet of medical things
Bibliographic record
Abstract
Authors and Affiliations Department of Computer Science and Software Engineering, Al Ain University, Abu Dhabi, 15322, UAE Yazeed Yasin Ghadi Department of Computer Science, Virtual University of Pakistan, Lahore, 55150, Pakistan Tehseen Mazhar Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, 57000, Pakistan Tariq Shahzad School of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia Muhammad Amir khan AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar Alaa Abd‑Alrazaq & Arfan Ahmed Faculty of Engineering, Université de Moncton, Moncton, NB, E1A3E9, Canada Habib Hamam School of Electrical Engineering, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, 2006, South Africa Habib Hamam Hodmas University College, Taleh Area, Mogadishu, Somalia Habib Hamam Bridges for Academic Excellence, Tunis, Tunisia Habib Hamam Authors Yazeed Yasin Ghadi View author publications Search author on: PubMed Google Scholar Tehseen Mazhar View author publications Search author on: PubMed Google Scholar Tariq Shahzad View author publications Search author on: PubMed Google Scholar Muhammad Amir khan View author publications Search author on: PubMed Google Scholar Alaa Abd‑Alrazaq View author publications Search author on: PubMed Google Scholar Arfan Ahmed View author publications Search author on: PubMed Google Scholar Habib Hamam View author publications Search author on: PubMed Google Scholar Corresponding authors Correspondence to Tehseen Mazhar or Arfan Ahmed .
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".