A Need for Standardized Reporting: A Scoping Review of Bioretention Research 2000–2019
Bibliographic record
Abstract
Bioretention cells are a type of low-impact development technology that, over the past two decades, have become a critical component of urban stormwater management. Research into bioretention has since proliferated, with disparate aims, intents and metrics used to assess the “performance” of bioretention cells. We conducted a comprehensive, systematic scoping review to answer the question of “How is the field performance of bioretention assessed in the literature?”, with the aim of understanding (1) how is the performance of bioretention defined in the literature? (2) what metrics are used to assess actual and theoretical performance? A review of 320 studies (mostly peer reviewed articles) found that performance was defined in terms of hydrologic controls, while investigations into water quality pathways and mechanisms of contaminant transport and fate and the role of vegetation were lacking; additionally, long term field and continuous modelling studies were limited. Bioretention field research was primarily conducted by a small number of institutions (26 institutions were responsible for 50% of the research) located mainly in high income countries, particularly Australia and the United States. We recommend that the research community (I) provide all original data when reporting results, (II) prioritize investigating the processes that determine bioretention performance and (III) standardize the collection, analysis and reporting of results. This dissemination of information will ensure that gaps in bioretention knowledge can be found and allow for improvements to the performance of bioretention cells around the world.
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 | 0.410 | 0.653 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.056 | 0.052 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.010 | 0.010 |
| 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; 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".