Freshwater mussel abundance and species richness: GIS relationships with watershed land use and geology
Bibliographic record
Abstract
We tested the hypotheses that mussel species richness and density are related to landscape features of watersheds. Measures of species richness and mussel density were estimated at 118 sites in 36 watersheds in the state of Iowa, U.S.A., a landscape characterized by >90% agricultural development. Geographical Information Systems (GIS) and regression analyses examined seven land use categories and nine geological descriptors, determining that both mean density and species richness were best correlated with mean watershed slope and the prevalence of alluvial deposits. Our analyses imply that agricultural watersheds with high slopes impact mussel abundance and richness through siltation and destabilization of stream substrate. Because alluvial deposits improve groundwater flux to streams, results suggest that relatively stable stream flows in alluvial watersheds improve mussel persistence. A second set of 82 observations on 38 independent watersheds corroborates the analyses, although historical and local impacts cause correlations between new observations and predictions to be weak.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".