Using thalweg profiling to assess and monitor juvenile salmon (<i>Oncorhynchus </i>spp.) habitat in small streams
Bibliographic record
Abstract
Thalweg profiles are longitudinal profiles of the streambed elevation measured along the deepest portion of the stream. This technique has recently been advocated as a tool to assess and monitor fish habitat in streams because metrics calculated from thalweg profiles can provide useful information on habitat quality, and measurements are both repeatable and independent of stream flow. Relations between thalweg metrics and land use have also been documented. However, a relation between fish abundance and thalweg metrics has not been established. To develop this relation, we surveyed thalweg profiles and sampled juvenile Chinook salmon (Oncorhynchus tshawytscha) density in 14 reaches of small tributary streams of the upper Yukon River. Chinook salmon density was correlated with three thalweg metrics. Two of these metrics length in residual pool and mean maximum residual pool depth provided useful measures of pool extent and quality and useful information on Chinook salmon habitat. Thalweg metrics differed between these undisturbed streams and reaches in streams affected by placer gold mining. These results suggest that thalweg profiling provides a useful tool to assess and monitor fish habitat in small streams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".