Supporting the Next Generation of Scientists to Lead Cancer Immunology Research
Bibliographic record
Abstract
Recent success in the use of immunotherapy for a broad range of cancers has propelled the field of cancer immunology to the forefront of cancer research. As more and more young investigators join the community of cancer immunologists, the Arthur L. Irving Family Foundation Cancer Immunology Symposium provided a platform to bring this expanding and vibrant community together and support the development of the future leaders in the field. This commentary outlines the lessons that emerged from the inaugural symposium highlighting the areas of scientific and career development that are essential for professional growth in the field of cancer immunology and beyond. Leading scientists and clinicians in the field provided their experience on the topics of scientific trajectory, career trajectory, publishing, fundraising, leadership, mentoring, and collaboration. Herein, we provide a conceptual and practical framework for career development to the broader scientific community.
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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 | 0.066 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.027 | 0.018 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".