Using continuous surveys to evaluate precision and bias of inferences from design-based reach-scale sampling of stream habitat
Bibliographic record
Abstract
Accurately estimating stream channel characteristics is essential for managing and restoring populations and aquatic ecosystems. Reach-based sampling designs have been used extensively to collect fisheries related data; however, few studies have examined accuracy and precision of scaling up reach-based sampling designs to stream habitat assessments. Here, we used continuous habitat surveys across multiple reaches to census stream attributes in tributaries in the upper Lewis River, Washington and better understand the potential bias and precision of reach-based designs. We used these continuous data to create simulated outcomes from three different random sampling designs. We found precision of estimates of stream-level habitat attributes (large woody debris, residual pool depth, and grain size) increased with the number of reaches sampled (i.e., sampling intensity); however, effort needed to achieve reasonable precision (coefficient of variation = 0.20) varied across streams, attributes, and designs. Bias (i.e., estimate—the truth) was relatively low, but also varied across streams and attributes. Our findings illustrate the challenges of using reach-based designs for stream-level habitat assessments and the need for novel approaches for broader data collection.
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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 | 0.097 | 0.289 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".