Testing unmanned aircraft systems for salmon spawning surveys
Bibliographic record
Abstract
Unmanned aircraft systems (UASs) were tested for counting Chinook salmon ( Oncorhynchus tshawytscha) redds as a more accurate, safer alternative to manned helicopter flights. Counting redds from the helicopter was less expensive and time consuming, but of the total redds counted at selected sites with a UAS, an average (± SD) of only 77% ± 14% was counted from the helicopter. A river-wide census of redds was not possible with a UAS because the study area was too large for the single field crew to survey. Simulation analyses were used to compare stratified random sampling (STRS) and sampling proportional to size (PPS) for estimating annual total redd counts from data collected with a UAS. The STRS estimates were more accurate and precise, whereas the PPS estimates, though biased, had 95% CIs that included the observed redd count more frequently. We strongly recommend that researchers conduct simulation analyses to evaluate alternative survey sampling methods if they are considering replacing census counts made from manned aircraft with counts estimated from data collected with a UAS. We conclude that UAS application reduces the risk inherent to manned aircraft flights, but the reduction in risk can come at the cost of estimates of population parameters that can sometimes be inaccurate and lack 95% CI coverage.
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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 | 0.004 | 0.012 |
| 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 | 0.001 |
| 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".