Remapping Hydroecoregion Boundaries: A Proposal for Improving the Base of the Running Water Monitoring Procedures
Bibliographic record
Abstract
Inland waters are constituted by a lot of seriously threatened habitats. The increasing need to safeguard these ecosystems led European Union Member States to propose the Water Framework Directive which decided the creation of homogeneous areas characterized by very similar geology, topography and climate, known as hydroecoregions (HER) and firstly proposed by the French National Research Institute of Science and Technology for Environment and Agriculture (Cemagref). Watercourses reference conditions had to be defined within any HER in order to confront any sampling site. HERs are consistent with European scale maps but important local reinterpretations in order to define more precise boundaries and extensions for each hydroecoregion are required and this point constitutes the main goal of this manuscript.Latium is a climatically very homogeneous region and it’s roughly divided into three major portions confirming Cemagref’s proposal. Geological and topolographical maps were then used in order to achieve a more detailed characterization of this region in order to obtain a more defined map. All our results allow to better define similarities and differences both between streams and within the same stream allowing to entirely locate each water course within the same HER. It would be important to follow up this study by proposing a similar approach for the entire national territory based on an appropriate region knowledge.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".