Author Correction: Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers
Bibliographic record
Abstract
In the originally published version of this Article there were three labeling errors in Fig. 1. The cluster names for “CD8-MHCII” and “CD8-proliferating” were switched and the cluster names for “Stem-like memory” and “MAIT” were switched (Fig. 1c). Their positions have now been corrected. In Fig. 1d, a typo in the “ SCL4A10 ” gene label has been corrected to “ SLC4A10 .” The legend for Fig. 1c originally referred to the heatmap as displaying the “top-5” most differential genes. This has been corrected to “the top-3” most differential genes. In the eighth paragraph of main text, a typo in the gene name “ LINC02246 ” has been corrected to “ LINC02446 .” Further, in Supplementary Table 9, the data point “5” was missing in the Number of nonsynonymous mutations per exome column for patient NY016-007; this has now been corrected. The original Article has been corrected online.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.040 | 0.020 |
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".