Modeling freshwater mussel distribution in relation to biotic and abiotic habitat variables at multiple spatial scales
Bibliographic record
Abstract
The habitat requirements of many native freshwater mussels remain unclear despite their imperiled status and ecological importance. To explore scale-specific habitat associations in the three genera of mussels found in the western United States (Anodonta, Gonidea, and Margaritifera) we used a multiscale random forest modeling approach to assess functional habitat parameters throughout a 55 km segment of the upper Middle Fork John Day River in northeastern Oregon. We characterized mussel occurrence and density with respect to the hierarchical, hydrogeomorphic structure by sampling reaches of varying valley confinement and channel units nested within individual reaches. Each genus exhibited unique longitudinal trends and channel unit-use patterns. In particular, the large-scale longitudinal trends in Margaritifera occurrence were associated with hydrogeomorphic characteristics at the reach and channel unit scale, with Margaritifera densities peaking in narrow valley segments and in riffles and runs. At the scale of the channel unit, all mussel genera responded to variation in physical habitat characteristics, particularly those that indicated more stable parts of the channel. Our results suggest that spatial patterns in freshwater mussels are associated with the hierarchical structuring of the lotic ecosystem and may provide guidance to restoration efforts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".