Empirical Characterization Factors for Life Cycle Assessment of the Impacts of Reservoir Occupation on Macroinvertebrate Richness across the United States
Bibliographic record
Abstract
The transformation of a river into a reservoir and the subsequent occupation of the riverbed by a reservoir can impact freshwater ecosystems and their biodiversity. We used the National Lake Assessment (134 reservoirs) and the National Rivers and Streams Assessment (2062 rivers and streams) of the United States Environmental Protection Agency in order to develop empirical characterization factors (CFs; in Potentially Disappeared Fraction of species [PDF]) evaluating the impacts of reservoir occupation on macroinvertebrate richness (number of taxa) at the reservoir, ecoregion and country spatial scales, using a space-for-time substitution. We used analyses of variance, variation partitioning, and multiple regression analysis to explain the role of ecoregion (or regionalization; accounting for spatial variability) and other potentially influential variables (physical, chemical and human), on PDFs. At the United States scale, 28% of macroinvertebrate taxa disappeared during reservoir occupation and PDFs followed a longitudinal gradient across ecoregions, where PDFs were higher in the west. We also observed that high elevation, oligotrophic and large reservoirs had high PDF. This study provides the first empirical macroinvertebrate-based PDFs for reservoir occupation to be used as CFs by LCA practitioners. The results provide strong support for regionalization and a simple empirical model for LCA modelers.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".