From Research to Operational Biomonitoring of Freshwaters: A Suggested Conceptual Framework and Practical Solutions
Bibliographic record
Abstract
The contradictory demands of managers (quick relevant operational responses) and ecologists (need time for in-depth research) involved in freshwater biomonitoring are still relevant today. To contribute to solving this dilemma, we are proposing a novel biomonitoring approach, which among many others, could be used in this field and further developed in the future. Biomonitoring actions are integrated in conceptual schemes, in which hydrology, chemistry, hydrogeology and geomorphology bear as much importance as biology. Among biomonitoring tools, a harmonization system allows end-users to use a set of qualitative indicators (various biotic indices) and integrate the information given by individual biotic indices. Functional traits and calculation of an ecological potential in porous aquatic habitats (surficial coarse sediments and the hyporheic system) are regarded as a basis for assessing ecological functioning of streams and rivers. This last methodology takes into account the dynamics of water exchanges between surface water and groundwater. Objectives of ecological quality, ecological potentials and resilience capacity that need to be preserved or rehabilitated in aquatic habitats are established. In lakes, a similar approach was followed and a general typology of lake functioning was proposed, including that for urban lakes. All those biomonitoring tools are transferred to end-users and subject to further research. The final purpose is to promote practical high-tech tools which are continually and interactively connected with ongoing research.
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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 | 0.020 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.006 |
| 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".