An Empirical Approach for Estimating the Precision of Hydroacoustic Fish Counts by Systematic Hourly Sampling
Bibliographic record
Abstract
Abstract Systematic hourly sampling is a logistically favorable method for hydroacoustic estimation of fish passage through constricted passageways or finite sections of a river monitored by fixed-location sonar systems. Similar to simple random and hourly stratified random sampling, systematic hourly sampling produces unbiased estimates of fish passage. Variances of estimates produced by systematic hourly sampling are determined by the sampling fraction, f, variance of the underlying process, S2, and correlations among all hourly strata of the process. This intrahour correlation dependency makes it difficult to accurately evaluate the precision of the estimate when the complete temporal pattern of the process is unavailable. Fish passage is rarely counted continuously, so the uncertainty of estimated mean by systematically subsampled fish counts is traditionally estimated using variance estimators. Variance estimates by these estimators are likely biased and subject to potentially large errors. We present an alternative approach for estimating the precision of systematic hourly sampling using an empirical relation between precision and sampling fraction established from continuous fish counts acquired by imaging sonar for a wide range of migration scenarios of Pacific salmon Oncorhynchus spp. in the lower Fraser River. The empirical relation indicates that a CV (100·SD/mean) of 5.5% can be achieved by the systematic hourly counting of fish passage at a counting effort of 10 min/h, while increasing the counting effort to 20 min/h only leads to a marginally improved CV of 4.1%. The data-based analysis also shows that the precision of systematic sampling can be gained more efficiently and consistently by increasing the sampling rate than by lengthening the sampling time. Received June 6, 2013; accepted January 21, 2014
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 | 0.010 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".