<scp>FreshLanDiv</scp>: A Global Database of Freshwater Biodiversity Across Different Land Uses
Bibliographic record
Abstract
ABSTRACT Motivation Freshwater ecosystems have been heavily impacted by land‐use changes, but data syntheses on these impacts are still limited. Here, we compiled a global database encompassing 241 studies with species abundance data (from multiple biological groups and geographic locations) across sites with different land‐use categories. This compilation will be useful for addressing questions regarding land‐use change and its impact on freshwater biodiversity. Main Types of Variables Contained The database includes metadata of each study, sites location, sample methods, sample time, land‐use category and abundance of each taxon. Spatial Location and Grain The database contains data from across the globe, with 85% of the sites having well‐defined geographical coordinates. Major Taxa and Level of Measurement The database covers all major freshwater biological groups including algae, macrophytes, zooplankton, macroinvertebrates, fish and amphibians.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.020 | 0.040 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.013 |
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".