Climate and connectivity mediate overwintering habitat suitability for centrarchids in a large floodplain river network
Bibliographic record
Abstract
Availability of suitable overwintering habitat is crucial for the survival of centrarchids in large floodplain rivers. However, there remains uncertainty in the spatiotemporal drivers of suitable conditions. We paired hydrogeomorphic characteristics with environmental data from 1994 to 2018 in individual lentic areas to (1) assess overwintering habitat availability throughout the Upper Mississippi River System using existing habitat suitability indices (HSIs) and (2) explore potential drivers of overall habitat suitability (HSI O ) and its components (dissolved oxygen, temperature, and flow). We found that flow velocities that exceeded suitable thresholds were independently responsible for 53% of nonsuitable habitats, and connectivity with lotic channels and river discharge increased velocity within lentic habitats. Additionally, colder winter conditions reduced water temperature, reducing availability of highly suitable habitat. Our results indicate that although warmer winters could increase the availability of highly suitable habitat for centrarchids, changes in flow regimes could lead to more connected areas becoming unsuitable. Our results provide critical information on factors that can be prioritized to manage centrarchid habitat, which is especially important in the context of uncertain future climate.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".