Evidence of the impacts of pharmaceuticals on aquatic animal behaviour: a systematic map protocol
Bibliographic record
Abstract
Abstract Background Globally, there is growing concern over the impacts of pharmaceuticals and drug manufacturing on aquatic animals, and pharmaceuticals are now recognized as contaminants of emerging environmental concern. In recent years, scientists, environmental managers, and policymakers have been interested in using behavioural endpoints for chemical regulation, given their importance for fitness and survival. The body of research on whether and how pharmaceutical exposure alters the behaviour of aquatic animals has grown exponentially, making it difficult to get an overview of the results. With an international spotlight on the management of these environmental threats, synthesizing the currently available data is vital to inform managers and policymakers, as well as highlighting areas where more research is needed. This is a protocol for a systematic evidence map (SEM) and serves as an a priori record of our objectives and methodological decisions. Our objectives are to identify, catalogue, and present primary research articles on the effects of human and veterinary pharmaceuticals on aquatic animal behaviour. Methods The literature search will be conducted using two electronic databases: Web of Science and Scopus, and we will supplement these searches with additional sources. The search string has been developed using a Population–Exposure–Comparison–Outcome (PECO) framework, to capture articles that used an aquatic organism (P, population) to test the effects of a pharmaceutical (E, exposure) on behaviour (O, outcome). Eligible articles must also have a control group (C, comparison). Articles will be screened in two stages, title and abstract, followed by full-text screening before data extraction. Decision trees have been designed a priori to appraise articles for eligibility at both stages of screening. At both stages, screening each article will be completed by two independent reviewers. Study validity will be appraised but not used as a basis for article inclusion. The information extracted from the eligible articles, along with bibliometric data, will be mapped and displayed. All data associated with this SEM will be publicly available through the Open Science Framework (OSF) and a future project webpage.
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 | 0.068 | 0.124 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.033 | 0.022 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.011 |
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".