Charting a course for freshwater biomonitoring: The grand challenges identified by the global scientific community
Bibliographic record
Abstract
The past 50 years have seen biomonitoring emerge as an essential means of generating the knowledge needed to inform protection and restoration of freshwater ecosystems. Despite the successes of biomonitoring, most freshwater ecosystems remain unmonitored. Moreover, degradation of freshwaters continues at a rapid rate with new threats and novel stressors emerging that are difficult to assess using existing techniques. New technologies and techniques have been developed to improve biomonitoring, but application has been slow and integration with existing approaches is often problematic. Clearly, freshwater biomonitoring faces many important challenges that must be addressed to meet management needs of the coming decades. We identify Grand Challenges facing freshwater biomonitoring with the aim of encouraging research and practice to address these challenges. We asked 256 biomonitoring scientists from around the globe to identify what they considered the most important challenges. From their submissions we established five Grand Challenges and 18 associated subchallenges. For each Grand Challenge, we outline the current state of biomonitoring practice and suggest promising pathways and approaches to address them. By identifying and describing these challenges, we strive to position freshwater biomonitoring to take advantage of emerging opportunities and enhance its capacity to meet current and future management needs.
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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 | 0.063 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.016 | 0.027 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".