Methods for Estimating Abundance and Associated Uncertainty from Passive Count Technologies
Bibliographic record
Abstract
Abstract Passive count technologies (e.g., resistivity counters, infrared cameras, and sonar/hydroacoustic cameras) are increasingly being used to enumerate migratory fish populations, but methodologies for converting counts into abundance estimates with uncertainty are not available. Passive counters are typically paired with a secondary data collection method, such as video, images, or direct observation, to validate or correct the count data for false positives and false negatives. We developed a framework that incorporates measurement error into passive counter estimates based on a statistical comparison with validation data. We demonstrate this framework using resistivity counter and video validation data collected for Gates Creek Sockeye Salmon Oncorhynchus nerka as they migrated through a fish passage facility at the Seton Dam in British Columbia, Canada. We also conducted simulations to evaluate the trade-offs between validation effort and accuracy and precision of abundance estimates, which can be used to plan passive counter postprocessing and validation. We found our method to be accurate and precise when abundance was high (i.e., >1,000), even when validation effort was low (i.e., 5% validation). There was a positive estimation bias when abundance was low (i.e., 100), and a minimum of 25% validation was required to achieve a CV of 15% and relative error less than 10%. When estimating abundance for small populations, higher validation effort is required to obtain sufficient precision in abundance estimates. Measurement error should not be overlooked in passive fish count technologies, and we provide a robust method for generating uncertainty in abundance estimates that increases their utility for population assessment and conservation.
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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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".