River reach types as large-scale biodiversity proxies for management: The case of the Greater Mekong Region
Bibliographic record
Abstract
Large-scale development projects such as hydropower dams in the Greater Mekong Region (GMR) exert high pressure on freshwater resources. Environmental impact assessments in the region can help to understand the possible impacts of these projects, yet these assessments typically require biodiversity data that can be both costly and time-intensive to acquire. As a substitute, researchers often assume that river or ecosystem classes based on geophysical characteristics can be used as biodiversity proxies in large-scale assessments to account for a lack of biodiversity data. However, only limited research exists that compares the spatial distribution of river classes and fish species, and therefore it remains unclear how well river classes can represent fish assemblages or, more generally, biodiversity. To address this question, we here compared a new river reach classification, which used regional expert knowledge to build the classes, with a dataset of fish species distribution in the GMR. We conducted a Redundancy Analysis to estimate how much variability in the fish species data can be explained by the river reach types. The results show a moderate correlation between the datasets (adjusted R2 of 0.44). Based on these findings, we elaborate on the role of spatial hierarchy in fish species distribution and discuss possible implications for management and policies in the GMR.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".