Predicting warming-induced hypoxic stress for fish in a fragmented river channel using ecosystem metabolism models
Bibliographic record
Abstract
Aquatic biota often face multiple anthropogenic threats such as river fragmentation and climate change that can contribute to high rates of aquatic species imperilment world-wide. Temperature-induced hypoxia is one under-explored mechanism that can threaten aquatic species in fragmented rivers with reduced flows. We applied ecosystem metabolism models to define the effect of water temperature on net ecosystem production (NEP) of oxygen at 12 sites of a fragmented river channel that supports three fish species at risk and experiences hypoxia. We found that water temperature and precipitation events at 75% of our sites were significantly and negatively correlated to NEP estimates and explained 28% of the variation in NEP within sites. Temperature-induced reductions in NEP at these sites likely contributed to hypoxic conditions threatening the three species at risk as NEP explained 41% of the variation in dissolved oxygen near all sites. Our results have applications for understanding drivers of hypoxic stress in fragmented watercourses, integrating water temperature–NEP effects with oxygen demands of sensitive fish species, and modeling future effects of climate change on aquatic species.
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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 | 0.001 | 0.001 |
| 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".