Inconsistent Regulatory Mapping Quietly Threatens Rivers and Streams
Bibliographic record
Abstract
Even the most stringent environmental law cannot protect a river if its tributaries remain exposed to pollution and other threats upstream. Excluding a subset of watercourses from legal protection therefore threatens to alter freshwater ecosystems across entire river networks and the services they provide, such as drinking water and flood regulation. Considerable attention has been devoted to defining the scope of environmental laws protecting watercourses. Yet how these definitions are implemented through regulatory mapping, the cartography of waterbodies that legally qualify as watercourses and are thus protected, has not been examined outside of the United States. Here, we demonstrate the consequences of regulatory mapping on the extent of river networks that are protected, using France as a case study. By assembling the first map of France's watercourses protected under the Water Law, we estimate that a quarter of previously mapped hydrographic segments were excluded from protection and found stark geographical variations in the extent of protected ecosystems. Headwater and nonperennial segments are disproportionately excluded by 28% compared to their prevalence (67%) in the overall hydrographic network, with potentially far-reaching implications for biodiversity and people. We expect regulatory frameworks in most countries to be equally susceptible to local interpretation of legal definitions.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".