Bibliographic record
Abstract
Cancer and Clinical Oncology wishes to acknowledge the following individuals for their assistance with peer review of manuscripts for this issue. Their help and contributions in maintaining the quality of the journal is greatly appreciated. Cancer and Clinical Oncology is recruiting reviewers for the journal. If you are interested in becoming a reviewer, we welcome you to join us. Please find the application form and details at http://www.ccsenet.org/reviewer and e-mail the completed application form to cco@ccsenet.org. Reviewers for Volume 7, Number 2 Aditya R Bele, University of Florida, USA Anand Kumar, Banaras Hindu University, India Chandra Sekhar Bathula, Washington State University, USA Dhaarini Murugan, Oregon Health and Science University, USA Hua Wang, Memorial Sloan Kettering Cancer Center, China Juan Luis Callejas Valera, UCSD/Moores Cancer Center, United States Kartik Anand, Houston Methodist Cancer Center, USA Kaushik Thakkar, Stanford University, USA Manal Mehibel, Stanford University, USA Mark G Trombetta, Drexel University College of Medicine, USA Mona Mostafa Mohamed, Cairo University, Egypt Rajesh Kumar, Cancer Center MGH/Harvard Medical School, USA Sunil Kumar, University of North Carolina at Chapel Hill, USA Wright Jacob, University of Glasgow, United Kingdom Xi Yang, Stanford University, USA
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".