Experiments from unfinished Registered Reports in the Reproducibility Project: Cancer Biology
Bibliographic record
Abstract
As part of the Reproducibility Project: Cancer Biology , we published Registered Reports that described how we intended to replicate selected experiments from 29 high-impact preclinical cancer biology papers published between 2010 and 2012. Replication experiments were completed and Replication Studies reporting the results were submitted for 18 papers, of which 17 were accepted and published by eLife with the rejected paper posted as a preprint. Here, we report the status and outcomes obtained for the remaining 11 papers. Four papers initiated experimental work but were stopped without any experimental outcomes. Two papers resulted in incomplete outcomes due to unanticipated challenges when conducting the experiments. For the remaining five papers only some of the experiments were completed with the other experiments incomplete due to mundane technical or unanticipated methodological challenges. The experiments from these papers, along with the other experiments attempted as part of the Reproducibility Project: Cancer Biology, provides evidence about the challenges of repeating preclinical cancer biology experiments and the replicability of the completed experiments.
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 | 0.226 | 0.454 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.039 | 0.027 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".