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 6, Number 2Aditya R Bele, University of Florida, USAAkhilesh Kumar, University of Wisconsin, USADhaarini Murugan, Oregon Health and Science University, USAHao Xie, Yale-New Haven Hospital Yale University School of Medicine, United StatesHemendra Mod, Aaruni Hospital PVT Ltd, IndiaJuan Luis Callejas Valera, UCSD/Moores Cancer Center, United StatesKaushik Thakkar, Stanford University, USAManal Mehibel, Stanford University, USAMark G Trombetta, Temple University School of Medicine, United StatesMohammed Abdelmoneam Osman, General Organization for Teaching Hospitals, EgyptMona Mostafa Mohamed, Cairo University, EgyptRajesh Kumar, Cancer Center MGH/Harvard Medical School, USASahar Ahmed Abdalbary, Cairo University, EgyptXin Wang, Sichuan University, China
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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 | 0.053 | 0.494 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.095 | 0.070 |
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".