Bibliographic record
Abstract
Molecular Oncology is now entering into its fifth year of existence. Gone are the early days where the journal struggled to establish itself while finding the best possible way to implement the vision underlying its foundation. Molecular Oncology aims to provide the scientific community with a platform where scientists can show-case personal opinions as well as outreaching their original research to other scientists. The journal intends to serve readers not only by publishing state-of-the-art research, but also by providing a coherent body of reviews, often combined into thematic issues, planned to broadly and comprehensively address timely topics. We offer this topical content at no charge, either to authors or readers. Molecular Oncology also aims at influencing the political agenda in translational cancer research by promoting current and near-future political news and debate. In order to ensure that our authors enjoy increased visibility we have frequent campaigns to highlight specific aspects of Molecular Oncology. Not only do we reach out by e-mail, but also have made concerted efforts to have personal contact with delegates encountered by at various cancer related conferences. Judging from the performance indicators that can sensibly be used to evaluate a journal, we can proudly affirm that Molecular Oncology is thriving. We have received our first Journal Citations Reports impact factor (Thomson Reuters) and are now indexed in all major scientific bibliographic databases, such as PubMed and Scopus. In addition, all other available indicators point towards continued growth in the journal's standing – the inflow of manuscripts and the number of downloads have increased substantially in the last months. We are also happy to note that Molecular Oncology's readership is on the rise. As the field of translational cancer research matures, the number of related research areas develops at an ever-growing pace and with it the number of researchers working in the various fields across the cancer continuum. To keep abreast of these new developments, particularly in the area of personalized cancer medicine, we have appointed two Senior Associated Editors, Anne-Lise Børresen-Dale (Oslo, Norway) and Richard Schilsky (Chicago, USA), who will provide advice on new areas to be covered by the journal and help to further establish Molecular Oncology within this competitive field. Moreover, we have appointed eleven new members to the Editorial Board – Ruedi Aebersold (Zurich, Switzerland), René Bernards (Amsterdam, The Netherlands), Stephen Baylin (Baltimore, USA), Hedvig Hricak, (New York, USA), Guido Kroemer (Paris, France), Ole C. Lingjaerde (Oslo, Norway), Tak W. Mak (Toronto, Canada), Elaine R. Mardis (St Louis, USA), Martine Piccart (Brussels, Belgium), Jorge S. Reis-Filho (London, UK), and Huanming Yang (Beijing, China) – whose joint expertise, together with that of our existing extensive network of collaborators will help guide the future development of the journal. We would like to take this opportunity to thank the members of the Editorial Board who are retiring this year for their commitment to the journal and for their invaluable assistance during our formative years. Finally, I would like to thank the readers for their ongoing support and to take this opportunity to invite you to submit research articles to Molecular Oncology. Looking forward to hearing from you soon and hoping to welcome you as one of our authors.
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.
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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.229 | 0.147 |
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".