Reproducibility of Scientific Claims in <i>Drosophila</i> Immunity: A Retrospective Analysis of 400 Publications
Bibliographic record
Abstract
Abstract Drosophila immunity has been the focus of intense study and has impacted other research fields including innate immunity and agriculturally or epidemiologically relevant investigations of insect pests and vectors. Unsurprisingly for such a large body of work, some published results were later found to be irreplicable. Although some results have been contradicted in the literature, many have no published follow-up, either due to a lack of research or low motivation to publish negative or contradictory results. We have addressed this by performing a reproducibility project that analyses the conceptual replicability of claims from articles published on Drosophila immunity before 2011. To assess replicability, we extracted claims from 400 articles on the Drosophila immune response to bacteria and fungi and performed preliminary verification by comparing these claims to other published literature in the field. Using alternative approaches, we also experimentally tested some ‘unchallenged’ claims, with no published follow-up. The intent of this analysis was to centralize evidence on insights and findings to improve clarity for scientists that may base research programs on these data. Although the aim of the ReproSci project is to assess the replicability of claims made in articles published in the field of Drosophila immunity, it is in no way an assessment of the ‘scientific value’ of the research. All our data are published on a publicly available website associated with this article ( https://ReproSci.epfl.ch/ ) that encourages community participation. This article provides a short summary of claims that were found to have contradictory evidence, which may help the community to assess past findings on Drosophila immunity and improve clarity going forward. Statistical analysis of this reproducibility study and metascience insights obtained from this approach are discussed in a companion article.
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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 | 0.177 | 0.542 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.055 | 0.051 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".