Assessing the reliability of ecotoxicological studies: An overview of current needs and approaches
Bibliographic record
Abstract
Abstract In general, reliable studies are well designed and well performed, and enough details on study design and performance are reported to assess the study. For hazard and risk assessment in various legal frameworks, many different types of ecotoxicity studies need to be evaluated for reliability. These studies vary in study design, methodology, quality, and level of detail reported (e.g., reviews, peer-reviewed research papers, or industry-sponsored studies documented under Good Laboratory Practice [GLP] guidelines). Regulators have the responsibility to make sound and verifiable decisions and should evaluate each study for reliability in accordance with scientific principles regardless of whether they were conducted in accordance with GLP and/or standardized methods. Thus, a systematic and transparent approach is needed to evaluate studies for reliability. In this paper, 8 different methods for reliability assessment were compared using a number of attributes: categorical versus numerical scoring methods, use of exclusion and critical criteria, weighting of criteria, whether methods are tested with case studies, domain of applicability, bias toward GLP studies, incorporation of standard guidelines in the evaluation method, number of criteria used, type of criteria considered, and availability of guidance material. Finally, some considerations are given on how to choose a suitable method for assessing reliability of ecotoxicity studies. Integr Environ Assess Manag 2017;13:640–651. © 2016 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC) Key Points When evaluating the reliability of an ecotoxicity study or individual effect values from this study, a systematic and transparent assessment method is critical to ensure an unbiased and consistent categorization of reliability. Eight different methods for reliability assessment were compared using a number of attributes. Some additional considerations are given on how to choose a suitable method for assessing reliability of ecotoxicity studies.
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 | 0.492 | 0.597 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.027 | 0.015 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".