Scientific integrity issues in Environmental Toxicology and Chemistry: Improving research reproducibility, credibility, and transparency
Bibliographic record
Abstract
High-profile reports of detrimental scientific practices leading to retractions in the scientific literature contribute to lack of trust in scientific experts. Although the bulk of these have been in the literature of other disciplines, environmental toxicology and chemistry are not free from problems. While we believe that egregious misconduct such as fraud, fabrication of data, or plagiarism is rare, scientific integrity is much broader than the absence of misconduct. We are more concerned with more commonly encountered and nuanced issues such as poor reliability and bias. We review a range of topics including conflicts of interests, competing interests, some particularly challenging situations, reproducibility, bias, and other attributes of ecotoxicological studies that enhance or detract from scientific credibility. Our vision of scientific integrity encourages a self-correcting culture that promotes scientific rigor, relevant reproducible research, transparency in competing interests, methods and results, and education. Integr Environ Assess Manag 2019;00:000-000. © 2019 SETAC.
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 | 0.084 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".