Evidence on the effects of flame retardant substances at ecologically relevant endpoints: a systematic map protocol
Bibliographic record
Abstract
Background Flame retardant (FR) substances are known to pose a risk to environmental health. A list of potential FR substances has been developed; however, detailed information on the risk, or hazard of such substances to the environment, specifically ecologically relevant endpoints involving animals, plants, bacteria and fungi, has not yet been collated.Methods The main objective of this study is to identify, organise and group existing primary evidence of the ecologically relevant (eco)toxicological effects of FR substances to the environment.Search Strategy We will search several databases across two electronic academic indexes (Scopus and Web of Science [All Collections]).Eligibility criteria Eligible studies must contain primary research investigating the risk (or hazard) of one or more included FR substances and study an ecologically relevant effect in any non-human animal, plant, bacteria and/or fungi. Ecologically relevant effects include impacts on growth, development, survival, reproduction and behaviour.Screening & extraction Articles will be screened at title and abstract, before a full-text review. All articles will be screened by a single reviewer, with a second reviewer assessing articles for consistency. Data extraction will be performed on all articles included at full text, with articles that do not meet the eligibility criteria excluded. All articles excluded at full text will be confirmed by a second reviewer.Study mapping & reporting Results will be published in a narrative summary and visualised in a publicly available, user-friendly, interactive and interrogable evidence map.
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 | 0.059 | 0.101 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.036 | 0.020 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.081 | 0.013 |
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".