Some like it slow: a bioenergetic evaluation of habitat quality for juvenile Chinook salmon in the Lemhi River, Idaho
Bibliographic record
Abstract
Management and conservation of freshwater habitat requires fine spatial resolution and watershed-scale and life-stage-specific methods due to complex linkages among land, climate, water uses, and aquatic organism necessities. In this study, we present a valley-scale microhabitat resolution, process-based bioenergetics approach that combines high-resolution topobathymetric LiDAR survey with two-dimensional hydrodynamic and bioenergetics modeling. We applied the model to investigate the role of lateral habitat, stream morphological complexity, water use, and temperature regimes on aquatic habitat quality distribution of juvenile Chinook salmon (Oncorhynchus tshawytscha) within the Lemhi River (eastern Idaho, USA). Modeling results showed two key aspects: (i) a reduction in diverted flows is not sufficient to improve habitat quality potentially because of a legacy of morphological simplification (directly due to straightening and wood removal and indirectly due to low in-channel flows) and (ii) morphological complexity and connectivity with side channels and margin areas, which are key and vital elements to support suitable habitats that meet or exceed energetic needs to sustain or promote growth of individuals and populations.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".