The global role of bioassessment in policy delivery and decision-making for inland waters
Bibliographic record
Abstract
Bioassessment is necessary to guide the management of freshwater ecosystems and promote sustainable water use. However, many countries either do not have nationally-approved bioassessment systems, or their bioassessment results are not used in water policy decision-making. Despite the importance and urgency of the topic, a global overview of bioassessment and its use in decision-making is missing. In this study, we analyzed survey responses from 341 bioassessment practitioners from 109 countries to examine the role bioassessment plays in freshwater governance. Two thirds of respondents reported that bioassessment was used in their country, with the strength of the link to legislation increasing with per capita Gross Domestic Product (GDP). Bioassessment data generally fulfilled several roles, including following trends over time and developing catchment plans, both significantly linked to increased per capita GDP. Strong relationships with stressors were cited as a strength of bioassessment over other forms of evidence gathering, whilst costs and shortage of trained staff were the most common weaknesses. Governance is key, being the main factor explaining why some countries don't have bioassessment and enabling its enaction through legislation in others. Initiatives therefore need to focus on creating an "enabling environment" as well as financial and technical aspects. We found synergies between having international agreements and using bioassessment for communication when delivering nationally important roles for bioassessment. By identifying key drivers for using bioassessment in decision making, our study provides recommendations to inform future research, policy and practice.
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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 | 0.033 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".