Compliance with and ecosystem function of biodiversity offsets in North American and European freshwaters
Bibliographic record
Abstract
Land-use change via human development is a major driver of biodiversity loss. To reduce these impacts, billions of dollars are spent on biodiversity offsets. However, studies evaluating offset project effectiveness that examine components such as the overall compliance and function of projects remain rare. We reviewed 577 offsetting projects in freshwater ecosystems that included the metrics project size, type of aquatic system (e.g., wetland and creek), offsetting measure (e.g., enhancement, restoration, and creation), and an assessment of the projects' compliance and functional success. Project information was obtained from scientific and government databases and gray literature. Despite considerable investment in offsetting projects, crucial problems persisted. Although compliance and function were related to each other, a high level of compliance did not guarantee a high degree of function. However, large projects relative to area had better function than small projects. Function improved when projects targeted productivity or specific ecosystem features and when multiple complementary management targets were in place. Restorative measures were more likely to achieve targets than creating entirely new ecosystems. Altogether the relationships we found highlight specific ecological processes that may help improve offsetting outcomes.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".