Lessons for the Next Generation of Scientists from the Second Annual Arthur and Sandra Irving Cancer Immunology Symposium
Bibliographic record
Abstract
The Arthur and Sandra Irving Cancer Immunology Symposium has been created as a platform for established cancer immunologists to mentor trainees and young investigators as they launch their research career in the field. By sharing their different paths to success, the senior faculty mentors provide an invaluable resource to support the development of the next generation of leaders in the cancer immunology community. This Commentary describes some of the key topics that were discussed during the 2022 symposium: scientific and career trajectory, leadership, mentoring, collaborations, and publishing. For each of these topics, established investigators discussed the elements that facilitate success in these areas as well as mistakes that can hinder progress. Herein, we outline the critical points raised in these discussions for establishing a successful independent research career. These points are highly relevant for 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 | 0.038 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".