Multiscale Processes Drive Formation of Logjam Habitats and Use by Juvenile Chinook Salmon Across a Boreal Stream Network in Alaska
Bibliographic record
Abstract
ABSTRACT Boreal forest streams are characterized by large volumes of instream wood, yet the relationship between logjams and Pacific salmon productivity remains underqualified. We located logjams ( n = 427) within the distribution of Chinook salmon ( Oncorhynchus tshawytscha ) in the Chena River, Alaska (Yukon River tributary) and measured dimensions, classified formative process, and snorkel‐sampled a subset ( n = 189) of logjams to detect and count juvenile salmon relative to multiscale variables and a dam. Logjam size increased downstream, whereas logjam density and large wood recruits declined (upstream = 6 logjams/km, 33 recruits/km; downstream = 0.3 logjams/km, 6 recruits/km), particularly below a dam that reduced downstream wood transport and log‐trapping locations (i.e., bars). Juvenile salmon occupied 68% of logjams; mid‐network logjams had the highest densities (mean = 0.85 fish/m 2 ). We modeled juvenile salmon counts with logjam‐, stream reach‐, and neighborhood‐scale (> 1 km) predictors. Covariates that best predicted juvenile salmon densities included bankfull flow and stream power at reach scales in addition to growth potential, spawning habitat quality, and logjam area within 1 km of the focal logjam at neighborhood‐scales. Multiscale perspectives that link landscape characteristics, wood dynamics, and instream modifications with juvenile salmon production will be important to facilitate conservation and management of boreal streams.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".